Computer-assisted Image Processing Research Articles

Genetic bases of resistance to the rice hoja blanca disease deciphered by a quantitative trait locus approach.

Rice hoja blanca (RHB) is one of the most serious diseases in rice-growing areas in tropical Americas. Its causal agent is RHB virus (RHBV), transmitted by the planthopper Tagosodes orizicolus Müir. Genetic resistance is the most effective and environment-friendly way of controlling the disease. So far, only 1 major quantitative trait locus (QTL) of Oryza sativa ssp. japonica origin, qHBV4.1, that alters the incidence of the virus symptoms in 2 Colombian cultivars has been reported. This resistance has already started to be broken, stressing the urgent need for diversifying the resistance sources. In the present study, we performed a search for new QTLs of O. sativa indica origin associated with RHB resistance. We used 4 F2:3-segregating populations derived from indica-resistant varieties crossed with a highly susceptible japonica pivot parent. Besides the standard method for measuring disease incidence, we developed a new method based on computer-assisted image processing to determine the affected leaf area (ALA) as a measure of symptom severity. Based on the disease severity and incidence scores in the F3 families under greenhouse conditions and SNP genotyping of the F2 individuals, we identified 4 new indica QTLs for RHB resistance on rice chromosomes 4, 6, and 11, namely, qHBV4.2WAS208, qHBV6.1PTB25, qHBV11.1, and qHBV11.2, respectively. We also confirmed the wide-range action of qHBV4.1. Among the 5 QTLs, qHBV4.1 and qHBV11.1 had the largest effects on incidence and severity, respectively. These results provide a more complete understanding of the genetic bases of RHBV resistance in the cultivated rice gene pool and can be used to develop marker-aided breeding strategies to improve RHB resistance. The power of joint- and meta-analyses allowed precise mapping and candidate gene identification, providing the basis for positional cloning of the 2 major QTLs qHBV4.1 and qHBV11.1.

Preliminary analysis of rock mass weathering grade using image analysis of CIELAB color space with the validation of Schmidt hammer: A case study

Rock slope instability causes rock slope failure with intense weathering processes in tropical regions. In addition to the various advanced numerical rock slope stability assessment techniques that have been developed thus far, simple evaluation techniques characterizing the rock slope weathering degree based on color changes of the rock surface remain one of the main techniques used by most geologists onto the rock slope stability assessment. The present study proposed a new evaluation method for weathering assessment of the limestone rock slope with computer-assisted image processing technologies to reduce human-induced errors based on a manual characterization process. The color changes of the limestone rock referring the CIELAB color space, which indirectly indicated the differential weathering of the rock mass, were measured in-situ using the FRU colorimeter with a 40 mm aperture. The Schmidt hammer was used to determine the intact rock strength and validate the color changes of the limestone rock to the International Society For Rock Mechanics (ISRM) weathering grade standard. Results indicated that a* and b* correlated strongly with R2 = 0.7404, underscoring the consistency of the CIEL*a*b color space data obtained in situ. The a* and b* correlated strongly with the uniaxial compressive strength result obtained via the rock Schmidt hammer test, with R2 of 0.7058 and 0.7011, respectively. The results were verified with the weathering grades analyzed using image analysis technique. In conclusion, the CIELAB color space is an effective tool in the preliminary assessment of rock mass weathering, particularly for slightly vegetated rock slope outcrops.

Psoriasis Skin Disease Identification Using Support Vector Machine(SVM) Image Classification and Determining the Growth Rate

In the Indian population, a larger part is under the subsistence level. Most of the people are living in areas of poor sanitation and have very little access to good medical facilities. From time to time, they don’t have the notice to go to a physician at the absolute time. The condition has been defined as a skin disorder or disease wherever there is a failure to induce the right identification and treatment in time typically ends up in advanced stages. Skin diseases tend to be itchy and cover the body easily. Among them, Psoriasis exists as a chronic inflammatory disease characterized by scaly patches on the skin. The proposed system focuses on SVM segmentation and scaling of 2D processed skin pore images of Psoriasis. The Feature Scaling Technique uses color, contrast, and image texture along with a combination of SVM classification features to diagnose and come up with a treatment solution. This computer-assisted image processing system removes erythematous from the psoriasis image for analysis and determination of growth rate. Therefore, earlier identification cuts back the symptoms of the illness and helps in developing a condition that indulges the strategies to live along with the disease condition called Psoriasis.

Computer-Assisted Image Processing System for Early Assessment of Lung Nodule Malignancy.

Simple SummaryLung cancer is the second most common cancer in men after prostate cancer and in women after breast cancer, but it is the leading cause of cancer death among both genders. This manuscript proposes a new computer-aided diagnosis system that uses only a single computed tomography scan to diagnose the pulmonary nodule as benign or malignant. This system helps in the early detection of the pulmonary nodules and shows its ability to identify the pulmonary nodules precisely.Lung cancer is one of the most dreadful cancers, and its detection in the early stage is very important and challenging. This manuscript proposes a new computer-aided diagnosis system for lung cancer diagnosis from chest computed tomography scans. The proposed system extracts two different kinds of features, namely, appearance features and shape features. For the appearance features, a Histogram of oriented gradients, a Multi-view analytical Local Binary Pattern, and a Markov Gibbs Random Field are developed to give a good description of the lung nodule texture, which is one of the main distinguishing characteristics between benign and malignant nodules. For the shape features, Multi-view Peripheral Sum Curvature Scale Space, Spherical Harmonics Expansion, and a group of some fundamental morphological features are implemented to describe the outer contour complexity of the nodules, which is main factor in lung nodule diagnosis. Each feature is fed into a stacked auto-encoder followed by a soft-max classifier to generate the initial malignancy probability. Finally, all these probabilities are combined together and fed to the last network to give the final diagnosis. The system is validated using 727 nodules which are subset from the Lung Image Database Consortium (LIDC) dataset. The system shows very high performance measures and achieves , , and for the accuracy, sensitivity, and specificity, respectively. This high performance shows the ability of the system to distinguish between the malignant and benign nodules precisely.

Accuracy of computer-aided image analysis in the diagnosis of odontogenic cysts: A systematic review.

Background This study aimed to search for scientific evidence concerning the accuracy of computer-assisted analysis for diagnosing odontogenic cysts. Material and Methods A systematic review was conducted according to the PRISMA statements and considering eleven databases, including the grey literature. Protocol was registered in PROSPERO (CRD 42020189349). The PECO strategy was used to define the eligibility criteria and only studies involving diagnostic accuracy were included. Their risk of bias was investigated using the Joanna Briggs Institute Critical Appraisal tool. Results Out of 437 identified citations, five papers, published between 2006 and 2019, fulfilled the criteria and were included in this systematic review. A total of 5,264 images from 508 lesions, classified as radicular cyst, odontogenic keratocyst, lateral periodontal cyst, glandular odontogenic cyst, or dentigerous cyst, were analyzed. All selected articles scored low risk of bias. In three studies, the best performances were achieved when the two subtypes of odontogenic keratocysts (solitary or syndromic) were pooled together, the case-wise analysis showing a success rate of 100% for odontogenic keratocysts and radicular cysts, in one of them. In two studies, the dentigerous cyst was associated with the majority of misclassifications, and its omission from the dataset improved significantly the classification rates. Conclusions The overall evaluation showed all studies presented high accuracy rates of computer-aided systems in classifying odontogenic cysts in digital images of histological tissue sections. However, due to the heterogeneity of the studies, a meta-analysis evaluating the outcomes of interest was not performed and a pragmatic recommendation about their use is not possible. Key words:Computer-assisted diagnosis, computer-assisted image analysis, computer-assisted image processing, odontogenic cysts, keratocysts, radicular cysts.

Angiotomografía en sospecha de enfermedad oclusiva aorto-ilíaca (o síndrome de Leriche)

Introducción. El síndrome de Leriche fue definido en 1940 por René Leriche como una enfermedad oclusiva aorto-ilíaca. Su importancia radica en que ocupa el segundo lugar en incidencia dentro de las enfermedades oclusivas arteriales, después de la enfermedad de la arteria femoral superficial. Objetivo. Presentar el caso clínico de un paciente con Síndrome de Leriche cuyo diagnóstico trombótico fue confirmado por una angiotomografía computarizada con su respectiva reestructuración en 3D. Adicionalmente, se informa al lector sobre los datos propios de la patología a través de una breve revisión de la literatura. Caso clínico. Paciente masculino con síntomas clínicos agudos de enfermedad oclusiva aorto-ilíaca o síndrome de Leriche en una extremidad. Se realiza el respectivo procesamiento de imagen asistida por computador (reconstrucción tridimensional) que evidencia la patología a pesar de que la ecografía Doppler no determinó trombosis inicial. Discusión. En procesos oclusivos arteriales se emplean técnicas no invasivas como la ecografía y la angiotomografía. Sin embargo, esta última es la técnica por excelencia, sobre todo para procesos trombóticos, pues agiliza la anticoagulación, así como el abordaje terapéutico. Conclusiones. La angiotomografía es una técnica no invasiva con alta sensibilidad y especificidad para detectar estenosis aorto-ilíaca. Se ha convertido en una gran herramienta diagnostica por sus alcances imagenológicos, como la obtención de imágenes iso volumétricas, que permiten evaluar todo el trayecto arterial en los diferentes planos, por medio del uso de medios de contraste, superando en resultados a la ecografía. 

Affordable Microfluidic Bead-Sorting Platform for Automated Selection of Porous Particles Functionalized with Bioactive Compounds

The ability to rapidly and accurately evaluate bioactive compounds immobilized on porous particles is crucial in the discovery of drugs, diagnostic reagents, ligands, and catalysts. Existing options for solid phase screening of bioactive compounds, while highly effective and well established, can be cost-prohibitive for proof-of-concept and early stage work, limiting its applicability and flexibility in new research areas. Here, we present a low-cost microfluidics-based platform enabling automated screening of small porous beads from solid-phase peptide libraries with high sensitivity and specificity, to identify leads with high binding affinity for a biological target. The integration of unbiased computer assisted image processing and analysis tools, provided the platform with the flexibility of sorting through beads with distinct fluorescence patterns. The customized design of the microfluidic device helped with handling beads with different diameters (~100–300 µm). As a microfluidic device, this portable novel platform can be integrated with a variety of analytical instruments to perform screening. In this study, the system utilizes fluorescence microscopy and unsupervised image analysis, and can operate at a sorting speed of up to 125 beads/hr (~3.5 times faster than a trained operator) providing >90% yield and >90% bead sorting accuracy. Notably, the device has proven successful in screening a model solid-phase peptide library by showing the ability to select beads carrying peptides binding a target protein (human IgG).

Comparison of Different Measurement Tools and Dimensional Parameters of Pterygium to Investigate its Impact on Refractive Indices and Ocular Aberrations

The aim of this study was to compare different measurement tools and parameters, including a new computer-assisted image processing technique for the quantitative analysis of the percentage of pterygium on the corneal surface, horizontal/vertical lengths obtained using slitlamp beam and surgical compass. A total of 21 pterygia of 17 patients were included in the study. The pterygia were measured by three different methods: a slitlamp beam, a surgical compass, and a new computer-assisted image processing method. Refractive indices and higher-order aberrations were analyzed in all cases. The new computer-assisted image processing technique revealed excellent intraclass correlation coefficients for intraobserver and interobserver reliability (0.999 and 0.995, respectively). However, horizontal and vertical lengths revealed more deviation between the measurements obtained with slitlamp beam and surgical compass. Although uncorrected visual acuity did not show any significant correlation between horizontal and vertical lengths of pterygia measured by either slitlamp beam or surgical compass, it was correlated with the digital pterygium ratio (rho, 0.462; P=0.035). All ocular aberration (total, higher-order, coma, trefoil, quatrefoil, spherical, and higher-order astigmatism) Root-mean-square values more strongly correlated with higher percentage values of pterygium that covers the cornea, measured by the new computer-assisted image processing technique. The percentage of pterygium covering the corneal surface seems to be more associated with the pterygium-related visual disturbances than with horizontal and vertical lengths measured by conventional techniques. Moreover, the new computer-assisted image processing technique can accurately and reliably measure the percentage extension of pterygium on cornea.

The correlation between HIF-1 alpha and VEGF in oral squamous cell carcinomas: Expression patterns and quantitative immunohistochemical analysis

BackgroundThe prognostic predict biomarkers are important in oral squamous cell carcinoma (OSCC). We investigated the expression patterns and quantitation of hypoxia-inducible factor-1 alpha (HIF-1α) and vascular endothelial growth factor (VEGF) in OSCC patients. Among OSCC patients with recurrence and metastasis, the expression percentages of HIF-1α and VEGF also were analyzed. MethodsThirty-eight patients (8 hyperkeratosis, 30 oral squamous cell carcinoma) were included in this study. In the follow-up period, 5 OSCC patients had metastasis and 12 OSCC patients had local recurrence. We used computer-assisted image processing to analyze immunohistochemistry (IHC). The quantitative analysis of IHC slides, including upper-layer epithelium (U) and lower-layer epithelium (L), was calculated. ResultsWe found homogeneous expression of VEGF in the epithelium. However, two patterns of HIF-1α expression were observed: homogeneous and heterogeneous. The highest U + L layer percentage in HIF-1α and VEF expression had significant association in tumor metastasis and recurrence (p<0.001 in HIF-1α and p<0.001 in VEGF). U + L layer HIF-1α expression percentage was >156.4%, and the survival rate was poor (p < 0.001). ConclusionHIF-1α expression was not only influenced by tumor hypoxia, it also reflected tumor cell characteristics. High concentrations of VEGF and HIF-1α may have value as prognostic markers of tumor metastasis and recurrence.

The characteristics of pressure injury photographs from the electronic health record in clinical settings.

To analyse and understand the characteristics of pressure injury images stored in the electronic health record. To improve the quality of pressure injury documentation, photographing pressure injuries and storing the images in the electronic health record are standard practices in many hospitals. As new technologies develop, the utilisation of computer-assisted image processing makes automatic measurement of pressure injury size and tissue segmentation through pressure injury images possible. The translation of new technological developments to pressure injury photography conducted in clinical environments faces obstacles such as the complexity of conditions in which photographs are taken. A cross-sectional descriptive study. A set of 360 pressure injury images were obtained from a hospital in western Pennsylvania. These images were taken in clinical settings during daily wound care service. The authors reviewed the pressure injury images to analyse the relative position of the pressure injury in the images, the shooting angle of the digital camera and the clinical objects in the background. Only 5.9% of the pressure injury images were confined to only the wound region. In 80.1% of the images, the pressure injury is located in the central part of the image. In 54.0% of the images, the lens of the digital camera was not pointed perpendicularly to the plane of the pressure injury. Bedside wound assessment of pressure injuries and assessment from photographs of pressure injuries display reasonable agreement with pressure injury staging. To extract wound information (e.g., size and tissue type) from pressure injury images through novel image processing technologies, the characteristics of pressure injury images in clinical settings should be better understood during the development of tools for pressure injury image processing. Our results can help image processing experts understand wound photography characteristics to shorten the gap between laboratory and clinical environments when translating new image processing technologies.

Contrast-Enhanced Magnetic Resonance Imaging of Gastric Emptying and Motility in Rats.

The assessment of gastric emptying and motility in humans and animals typically requires radioactive imaging or invasive measurements. Here, we developed a robust strategy to image and characterize gastric emptying and motility in rats based on contrast-enhanced magnetic resonance imaging (MRI) and computer-assisted image processing. The animals were trained to naturally consume a gadolinium-labeled dietgel while bypassing any need for oral gavage. Following this test meal, the animals were scanned under low-dose anesthesia for high-resolution T1-weighted MRI in 7 Tesla, visualizing the time-varying distribution of the meal with greatly enhanced contrast against non-gastrointestinal (GI) tissues. Such contrast-enhanced images not only depicted the gastric anatomy, but also captured and quantified stomach emptying, intestinal filling, antral contraction, and intestinal absorption with fully automated image processing. Over four postingestion hours, the stomach emptied by 27%, largely attributed to the emptying of the forestomach rather than the corpus and the antrum, and most notable during the first 30 min. Stomach emptying was accompanied by intestinal filling for the first 2 h, whereas afterward intestinal absorption was observable as cumulative contrast enhancement in the renal medulla. The antral contraction was captured as a peristaltic wave propagating from the proximal to distal antrum. The frequency, velocity, and amplitude of the antral contraction were on average 6.34 ± 0.07 contractions per minute, 0.67 ± 0.01 mm/s, and 30.58 ± 1.03%, respectively. These results demonstrate an optimized MRI-based strategy to assess gastric emptying and motility in healthy rats, paving the way for using this technique to understand GI diseases, or test new therapeutics in rat models.The assessment of gastric emptying and motility in humans and animals typically requires radioactive imaging or invasive measurements. Here, we developed a robust strategy to image and characterize gastric emptying and motility in rats based on contrast-enhanced magnetic resonance imaging (MRI) and computer-assisted image processing. The animals were trained to naturally consume a gadolinium-labeled dietgel while bypassing any need for oral gavage. Following this test meal, the animals were scanned under low-dose anesthesia for high-resolution T1-weighted MRI in 7 Tesla, visualizing the time-varying distribution of the meal with greatly enhanced contrast against non-gastrointestinal (GI) tissues. Such contrast-enhanced images not only depicted the gastric anatomy, but also captured and quantified stomach emptying, intestinal filling, antral contraction, and intestinal absorption with fully automated image processing. Over four postingestion hours, the stomach emptied by 27%, largely attributed to the emptying of the forestomach rather than the corpus and the antrum, and most notable during the first 30 min. Stomach emptying was accompanied by intestinal filling for the first 2 h, whereas afterward intestinal absorption was observable as cumulative contrast enhancement in the renal medulla. The antral contraction was captured as a peristaltic wave propagating from the proximal to distal antrum. The frequency, velocity, and amplitude of the antral contraction were on average 6.34 ± 0.07 contractions per minute, 0.67 ± 0.01 mm/s, and 30.58 ± 1.03%, respectively. These results demonstrate an optimized MRI-based strategy to assess gastric emptying and motility in healthy rats, paving the way for using this technique to understand GI diseases, or test new therapeutics in rat models.

Computer-assisted image processing 12 lead ECG model to diagnose hyperkalemia

BackgroundWe sought to develop an improved 12 lead ECG model to diagnose hyperkalemia by use of traditional and novel parameters. MethodsWe retrospectively analyzed ECGs in consecutive hyperkalemic patients (serum potassium (K)>5.3mEq/L) by blinded investigators with normokalemic ECGs as internal controls. Potassium levels were modeled using general linear mixed models followed by refit with standardized variables. Optimum sensitivity and specificity were determined using cut point analysis of ROC-AUC. ResultsThe training set included 236 ECGs (84 patients) and validation set 97 ECGs (23 patients). Predicted K=(5.2354)+(0.03434*descending T slope)+(−0.2329*T width)+(−0.9652*reciprocal of new QRS width>100msec). ROC-AUC in the validation set was 0.78 (95% CI 0.69–0.88). Maximum specificity of the model was 84% for K>5.91 with sensitivity of 63%. ConclusionECG model incorporating T-wave width, descending T-wave slope and new QRS prolongation improved hyperkalemia diagnosis over traditional ECG analysis.

Computer-assisted image processing to detect spores from the fungus Pandora neoaphidis

This contribution demonstrates an example of experimental automatic image analysis to detect spores prepared on microscope slides derived from trapping. The application is to monitor aerial spore counts of the entomopathogenic fungus Pandora neoaphidis which may serve as a biological control agent for aphids. Automatic detection of such spores can therefore play a role in plant protection. The present approach for such detection is a modification of traditional manual microscopy of prepared slides, where autonomous image recording precedes computerised image analysis. The purpose of the present image analysis is to support human visual inspection of imagery data – not to replace it. The workflow has three components:•Preparation of slides for microscopy.•Image recording.•Computerised image processing where the initial part is, as usual, segmentation depending on the actual data product. Then comes identification of blobs, calculation of principal axes of blobs, symmetry operations and projection on a three parameter egg shape space.

Photo-induction and automated quantification of reversible mitochondrial permeability transition pore opening in primary mouse myotubes.

Opening of the mitochondrial permeability transition pore (mPTP) is involved in various cellular processes including apoptosis induction. Two distinct states of mPTP opening have been identified allowing the transfer of molecules with a molecular weight <1500 Da or <300 Da. The latter state is considered to be reversible and suggested to play a role in normal cell physiology. Here we present a strategy combining live-cell imaging and computer-assisted image processing allowing spatial visualization and quantitative analysis of reversible mPTP openings (“ΔΨ flickering”) in primary mouse myotubes. The latter were stained with the photosensitive cation TMRM, which partitions between the cytosol and mitochondrial matrix as a function of mitochondrial membrane potential (ΔΨ). Controlled illumination of TMRM-stained primary mouse myotubes induced ΔΨ flickering in particular parts of the cell (“flickering domains”). A novel quantitative automated analysis was developed and validated to detect and quantify the frequency, size, and location of individual ΔΨ flickering events in myotubes.

Micromeritic procedures in assessing antinuclear antibody patterns in immunofluorescent assay.

The aim of this study was to introduce a micromeritic procedure (a statistical approach for small objects) in indirect immunofluorescence assay (IFA) to find objective quantitative parameters of antinuclear antibody (ANA) patterns which could support a diagnosis of auto-immune diseases. Sera of patients with systemic autoimmune diseases, McCoy-Plovdiv serum-free cell line, goat anti-human immunoglobulin-G FITC-conjugate, fluorescent microscope, computer-assisted digital image processing, analysis using a micromeritic procedure, ANOVA. Three ANA fluorescent patterns (homogeneous, rim and speckled) were analyzed by the micromeritic procedure. Parameters for the image brightness of the pixels (pixel grey value) were obtained and discussed as objective characteristics of fluorescent patterns: maximum ANA-linkage volume and surface density were established for the objects with speckled localization pattern. The micromeritic method for getting objective quantitative values of ANA fluorescent patterns in indirect immunofluorescent assay might be a valuable tool aiding in immunological diagnosing if integrated in a laboratory software package.

Quantification of Protein Expression on an Immunofluorescence Section by Using the MetaMorph Image Analysis System

In recent years, cancer treatment response and disease progression increasingly rely on immunofluorescence to monitor the changes in targeted antigens. Estimation of abundances of antigens has so far relied primarily on visual scoring and to a lesser extent on computer-assisted image processing techniques. The quantification of immunostaining by means of digital image analysis by using Leica MetaMorph AF 1.4 allows the characterization of protein expression revealed by immunohistochemistry on normal or pathological tissue slices. Our study focuses on the practical aspects of image acquisition, segmentation of staining and counterstaining areas, and extraction of quantitative features. We illustrate our findings by using a Red-Green-Blue (RGB) system to quantify different immunofluorescence markers targeting proteins with different expression patterns (cytoplasmic and membranous) in human breast cancer. Our investigations led us to identify several steps that we consider essential for standardizing computer-assisted immunostaining quantification experiments. In addition, we propose a data normalization process based on reference materials to compare measurements between different samples. In conclusion, we recommend certain critical prerequisites that commercial or in-house systems should satisfy to permit valid immunostaining quantification. In conclusion, image analysis-based quantitative immunofluorescence has the potential to accurately improve biomarker evaluation in research, clinical, and therapeutic applications for the study of cancer and other diseases.

Relationships between Tumor Volume and Lymphatic Metastasis and Prognosis in Early Oral Tongue Cancer

ObjectivesAlthough T stage is an important prognostic tool for oral tongue cancer, it fails to define the depth of invasion and true three-dimensional volume of primary tumors. The purpose of this paper is to determine the relations between tumor volume and lymph node metastasis and survival in early oral tongue cancer.MethodsForty-seven patients with T1-2 tongue cancer were included. Tumor volumes were measured by the computerized segmentation of T2-weighted magnetic resonance imaging.ResultsThe overall average tumor volume was 27.7 cm3 (range, 1.4 to 60.1 cm3). A significant positive correlation was found between tumor volume and pathological T stage, depth of invasion, and cervical lymph node metastasis (P<0.001, P<0.001, and P=0.002, respectively). When the tumor volume exceeded 20 cm3, the cervical metastasis rate increased to 69.2%. The overall 5-year disease specific survival rate was 80%. There was a statistically significant association between large tumor volume (≥20 cm3) and the 5-year disease-specific survival (P=0.046).ConclusionTumor volume larger than 20 cm3 was associated with greater risk cervical lymph node metastasis and poor 5-year disease-specific survival rate in early oral tongue cancer patients.

The Influence of Nerve Growth Factor (NGF) on Distribution of Perivascular Nerves in Tumor Neovasculatures of Mouse Corneal

Angiogenesis, or new blood vessel formation, is critical for the growth and spread of tumors. The vascular tone and tissue blood flow are maintained and regulated by perivascular nerves. However, many studies have reported that tumor neovascular vessels have no innervation of perivascular nerves. We have shown that nerve growth factor (NGF) facilitated perivascular innervation and suppressed the tumor growth. From these results, we hypothesized that the neuronal regulation of blood flow toward tumors via perivascular nerves may lead suppression of the tumor growth. Therefore, the aim of this study is to investigate effect of NGF on distribution of perivascular nerves and neovessel form in tumor tissues, which were generated by mouse corneal micropocket method. A gel, which contained DU145 prostate carcinoma cells, was implanted into the mouse corneal. NGF or saline was subcutaneously administered using an osmotic mini-pump. After 1 week, the distribution of perivascular nerves in mouse corneal were immunohistochemically studied. Also, the density of neovessels (immunocytochemically stained CD31) and smooth muscles (α-smooth muscle actin; SMA) in tumor tissues was quantified by the computer-assisted image processing. Four days after implantation of tumor cells in mouse corneal, many neovessels generated from corneal limbal vessels were observed in tumor tissues. Treatment of mouse with NGF resulted in innervation of perivascular nerves around tumor neovessels, but not observed in saline-treated group. NGF treatment increased SMA-, but not CD31-, immunopositive cells. These results suggest that NGF may facilitate innervations of perivascular nerve to regulate the blood flow in tumor neovessels.

Evaluation of Hepatic Cystic Echinococcosis’ CT image in Xinjiang Uygur Autonomous Region based on Kolmogorov Complexity Model

Designing and developing computer-assisted image processing techniques to help doctors improve their diagnosis has received considerable interests over the past years. In this paper, we used the kolmogorov complexity model to analyze the CT images of the healthy liver and multiple daughter hydatid cysts. Before the complexity characteristic calculating, the image preprocessing methods had been used for image standardization. From the kolmogorov complexity model, complexity characteristic were calculated in order to quantify the complexity, between healthy liver and multiple daughter hydatid cysts. Then we use statistical method to analyze the complexity characteristic of those two types of images. Our preliminary results show that the complexity characteristic has statistically significant (p<0.05) to analyze these two types CT images, between the healthy liver and the multiple daughter hydatid cysts. Furthermore, the result leads us to the conclusion that the kolmogorov complexity model could use for analyze the hydatid disease and will also extend the analysis the other lesions of liver.

Computational intelligence in biomedical imaging: multidimensional analysis of spatio-temporal patterns

Technical innovations in radiology, such as advanced cross-sectional imaging methods, have opened up new vistas for the exploration of structure and function of the human body enabling both high spatial and temporal resolution. However, these techniques have led to vast amounts of data whose precise and reliable visual analysis by radiologists requires a considerable amount of human intervention and expertise, thus resulting in a cost factor of substantial economic relevance. Hence, the computer-assisted analysis of biomedical image data has moved into the focus of interest as an issue of high priority research efforts. In this context, innovative approaches to exploratory analysis of huge complex spatio-temporal patterns play a key role to improve computer-assisted signal and image processing in radiology. Examples of such approaches are various unsupervised vector quantization methods or supervised function approximation techniques, such as Generalized Radial-Basis-Functions- (GRBF-) neural networks. Recent developments motivated by concepts of computational intelligence are the `Deformable Feature Map' (DM) as an algorithm for self-organized model adaptation, the `Mutual Connectivity Analysis' (MCA) as an instrument for the analysis of large time-series ensembles and the `Exploratory Observation Machine' (XOM) as a novel general framework for learning by self-organization--three methods that the author has invented and applied to biomedical real-world applications. This contribution covers both conceptual foundations and applications of such methods for pattern recognition and analysis to a wide scope of radiological data sets, such as structural and functional segmentation in Magnetic Resonance Imaging (MRI), ranging from functional MRI for human brain mapping to the monitoring of disease progression in multiple sclerosis by automatic lesion segmentation, as well as novel approaches to image time-series analysis in MRI mammography for breast cancer diagnosis. Current projects related to the modeling of speech production and to genome-wide expression analysis of microarray data in bioinformatics confirm the broad applicability of the presented methods.