Computer-based Image Analysis System Research Articles

Histological Evaluation of Alveolar Ridge Preservation Using Different Bone Grafts: Clinical Study Analysis Part II.

To compare histologically the percentage of bone formation 12-20 weeks after ridge augmentation using 2 different techniques. Tooth loss is associated with 3-dimensional bone remodeling and ridge atrophy. Ridge preservation procedures can prevent alveolar bone volume loss. Different techniques and materials are used to preserve the alveolar ridge. Computer-generated randomization software was used to assign 2 ridge preservation techniques for 11 extraction sites. In group I, type I bovine Achilles tendon collagen plugs with bioactive resorbable calcium apatite crystals (CPCAC) were placed, and in group II, cortico-cancellous bone chips (CCBC) mix and an expanded polytetrafluoroethylene (ePTFE) barrier membrane were placed. The histomorphometric studies were performed using a computer-based image analysis system (ImageJ 1.4, National Institute of Health, Bethesda, Md) to calculate the pixel area of bone tissue and the remaining bone graft material. The histomorphometric data were analyzed using a Student t test to compare the measurements between the 2 experimental groups. This parametric statistical test was employed to determine if there were any statistically significant differences in the quantitative histological parameters between the groups. The sockets that received CPCAC showed a lower (31.89%) percentage of native bone surface area compared with the CCBC group (43.87%). However, the difference was not statistically significant (P < .05). In addition, the CPCAC group showed evidence of foreign-body reaction. The CCBC graft covered with an ePTFE barrier may induce more bone formation with minimal inflammation in an extraction socket compared with a collagen plug with calcium apatite crystals. In addition, histological analysis of the CPCAC graft showed evidence of foreign-body reaction, which indicates a negative clinical impact.

Long-Term Supplementation of Canthaxanthin Does Not Inhibit Gastric Epithelial Cell Proliferation in Helicobacter mustelae-Infected Ferrets

The effect of canthaxanthin (CX), a non-provitamin A carotenoid, on gastric epithelial proliferation was studied in ferrets colonized with Helicobacter mustelae, which causes a chronic gastritis and an increased gastric epithelial cell proliferation. Seven spayed female ferrets were dosed by gavage with CX beadlets (50 mg/kg body wt, 5 d/wk) over 2 y, whereas seven control animals were given placebo beadlets. At the end of the 2-y-period, ferrets were killed, and gastric tissues were obtained from the antrum and body regions. A cell-proliferating biomarker, proliferating cell nuclear antigen (PCNA), was assayed by immunohistochemistry. The PCNA positivity was measured by a computer-based image analysis system. Serum concentrations of carotenoids, retinoids and tocopherols were analyzed by HPLC. Serum antioxidant status was measured by the oxygen radical absorbance capacity (ORAC) assay. The PCNA positivity in both antrum and body regions was not significantly different between CX-fed ferrets and controls. Serum CX concentrations were significantly higher in CX-fed ferrets vs. controls (P<0.001), whereas levels of other carotenoids, retinoids and tocopherols were not significantly different. The ORAC values were not significantly different between groups. This study does not suggest inhibitory effects of CX on gastric epithelial cell proliferation in H. mustelae infected ferrets.

Chest CT Images for COVID-19: Radiologists and Computer-Based Detection.

BackgroundCharacteristic chest computed tomography (CT) manifestation of 2019 novel coronavirus (COVID-19) was added as a diagnostic criterion in the Chinese National COVID-19 management guideline. Whether the characteristic findings of Chest CT could differentiate confirmed COVID-19 cases from other positive nucleic acid test (NAT)-negative patients has not been rigorously evaluated.PurposeWe aim to test whether chest CT manifestation of 2019 novel coronavirus (COVID-19) can be differentiated by a radiologist or a computer-based CT image analysis system.MethodsWe conducted a retrospective case-control study that included 52 laboratory-confirmed COVID-19 patients and 80 non-COVID-19 viral pneumonia patients between 20 December, 2019 and 10 February, 2020. The chest CT images were evaluated by radiologists in a double blind fashion. A computer-based image analysis system (uAI System, Lianying Inc., Shanghai, China) detected the lesions in 18 lung segments defined by Boyden classification system and calculated the infected volume in each segment. The number and volume of lesions detected by radiologist and computer system was compared with Chi-square test or Mann-Whitney U test as appropriate.ResultsThe main CT manifestations of COVID-19 were multi-lobar/segmental peripheral ground-glass opacities and patchy air space infiltrates. The case and control groups were similar in demographics, comorbidity, and clinical manifestations. There was no significant difference in eight radiologist identified CT image features between the two groups of patients. There was also no difference in the absolute and relative volume of infected regions in each lung segment.ConclusionWe documented the non-differentiating nature of initial chest CT image between COVID-19 and other viral pneumonia with suspected symptoms. Our results do not support CT findings replacing microbiological diagnosis as a critical criterion for COVID-19 diagnosis. Our findings may prompt re-evaluation of isolated patients without laboratory confirmation.

Augmented Reality Research of Measuring X-Ray Dental Film Alveolar Bone Based on Computer Image Analysis System.

The important application of computer imaging technology in the medical field is a necessary auxiliary method for clinical diagnosis and treatment. At present, many people are affected by various factors and have various problems caused by the dental cellular bone. Traditional treatment methods are complex and long, which can cause damage to body tissues. Based on this problem, this paper takes the augmented reality measurement of X-ray dental film as the research object. Based on the in-depth measurement algorithm of the computer image analysis system, two three-dimensional reconstruction methods based on the center of gravity and the matching of the front and side positions are proposed. These two methods only need two X-rays of the front and side of the dental film, the three-dimensional parameters are obtained through calculation and analysis of each spine in the X-ray film, and these parameters are used to fit the dental alveolar bone model. The experimental results prove that the computer-based image analysis system has a great effect on the measurement of X-ray dental film alveolar bone. The positive correlation coefficient reaches 0.87. Compared with the cerebral infarction caused by other methods, the proportion of people with dental film alveolar bone injury is about 15%; after treatment, the functional recovery rate reaches more than 80%. Studies have found that there is a great difference in the age of the population that needs to be treated for dental slices and alveolar bone. The grade of patients is generally under 20 and over 60. This shows that the measurement of X-ray dental film alveolar bone based on computer image analysis system can play an important role in protecting people's oral health.

CNN cascades for segmenting sparse objects in gigapixel whole slide images

Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, large amounts of digital image data are being generated. Accordingly, there is a strong demand for the development of computer based image analysis systems. Here, we address application scenarios in histopathology consisting of sparse, small objects-of-interest occurring in the large gigapixel images. To tackle the thereby arising challenges, we propose two different CNN cascade approaches which are subsequently applied to segment the glomeruli in whole slide images of the kidney and compared with conventional fully-convolutional networks. To facilitate unbiased evaluation, eight-fold cross-validation is performed and finally means and standard deviations are reported. Overall, with the best performing cascade approach, single CNNs are outperformed and a pixel-level Dice similarity coefficient of 0.90 is obtained (precision: 0.89, recall: 0.92). Combined with qualitative and further object-level analyses the obtained results are assessed as excellent also compared to previous approaches. We can state that especially one of the proposed cascade networks proved to be a highly powerful tool providing the best segmentation accuracies and also keeping the computing time at the lowest level. This work facilitates accurate automated segmentation of renal whole slide images which consequently allows fully-automated big data analyses for the assessment of medical treatments. Furthermore, this approach can also easily be adapted to other similar biomedical application scenarios.

How is plus disease diagnosed in ROP? Insights from a deep learning computer-based image analysis system with occlusion analysis

Diagnosis of plus disease in retinopathy of prematurity (ROP) is subjective and variable. We have shown that computer-based image analysis can diagnose plus disease with comparable or better accuracy than experts. This study uses outputs from a deep learning algorithm to identify vascular features considered most significant by experts for plus disease diagnosis.

Retrieve similar cell images in OpenSlide file

Computer-based image analysis system enables efficient retrieval of similar images from large-size pathology database. In such a system, images are expressed based on visual content characteristics, and similarities between images are obtained by comparing the features. A pathology image is usually very huge and expressed as several layer of image quality called OpenSlide. To find similar cells from a OpenSlide file, we have to use high performance computer equiped with multi-core and large size memory. In this paper, we propose a method to find similar cell images with resource limited computer. For this purpose, we exploit several technique to minimize system resource requirement and adapt imaging process scheme that enhances the accuracy of finding similar cell images from a OpenSlide file. We adapt a leveling, tiling and sub tiling to the OpenSlide file and extracting the feature points accurately using the hybrid feature extracting algorithm that adapts advantages of ORB and Blob algorithm. Furthermore, grayscale and histogram schemes are used to improve the accuracy of finding similar pathology cell images. Experiment results show that the proposed system improves the performance of the system and increases the accuracy of finding similar images efficiently.

Plus Disease in Retinopathy of Prematurity: Improving Diagnosis by Ranking Disease Severity and Using Quantitative Image Analysis

To determine expert agreement on relative retinopathy of prematurity (ROP) disease severity and whether computer-based image analysis can model relative disease severity, and to propose consideration of a more continuous severity score for ROP. We developed 2 databases of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP (i-ROP) cohort study and recruited expert physician, nonexpert physician, and nonphysician graders to classify and perform pairwise comparisons on both databases. Six participating expert ROP clinician-scientists, each with a minimum of 10 years of clinical ROP experience and 5 ROP publications, and 5 image graders (3 physicians and 2 nonphysician graders) who analyzed images that were obtained during routine ROP screening in neonatal intensive care units. Images in both databases were ranked by average disease classification (classification ranking), by pairwise comparison using the Elo rating method (comparison ranking), and by correlation with the i-ROP computer-based image analysis system. Interexpert agreement (weighted κ statistic) compared with the correlation coefficient (CC) between experts on pairwise comparisons and correlation between expert rankings and computer-based image analysis modeling. There was variable interexpert agreement on diagnostic classification of disease (plus, preplus, or normal) among the 6 experts (mean weighted κ, 0.27; range, 0.06-0.63), but good correlation between experts on comparison ranking of disease severity (mean CC, 0.84; range, 0.74-0.93) on the set of 34 images. Comparison ranking provided a severity ranking that was in good agreement with ranking obtained by classification ranking (CC, 0.92). Comparison ranking on the larger dataset by both expert and nonexpert graders demonstrated good correlation (mean CC, 0.97; range, 0.95-0.98). The i-ROP system was able to model this continuous severity with good correlation (CC, 0.86). Experts diagnose plus disease on a continuum, with poor absolute agreement on classification but good relative agreement on disease severity. These results suggest that the use of pairwise rankings and a continuous severity score, such as that provided by the i-ROP system, may improve agreement on disease severity in the future.

Plus disease: is it more than meets the ICROP?

Published definitions of “plus disease” in retinopathy of prematurity (ROP) reference only arterial tortuosity and venous dilation within the posterior pole. However, subsequent research has suggested that additional retinal features should be considered. This project uses a computer-based image analysis system (Imaging and Informatics in ROP, i-ROP) to identify features considered by experts when classifying plus disease.

Expert Diagnosis of Plus Disease in Retinopathy of Prematurity From Computer-Based Image Analysis.

Published definitions of plus disease in retinopathy of prematurity (ROP) reference arterial tortuosity and venous dilation within the posterior pole based on a standard published photograph. One possible explanation for limited interexpert reliability for a diagnosis of plus disease is that experts deviate from the published definitions. To identify vascular features used by experts for diagnosis of plus disease through quantitative image analysis. A computer-based image analysis system (Imaging and Informatics in ROP [i-ROP]) was developed using a set of 77 digital fundus images, and the system was designed to classify images compared with a reference standard diagnosis (RSD). System performance was analyzed as a function of the field of view (circular crops with a radius of 1-6 disc diameters) and vessel subtype (arteries only, veins only, or all vessels). Routine ROP screening was conducted from June 29, 2011, to October 14, 2014, in neonatal intensive care units at 8 academic institutions, with a subset of 73 images independently classified by 11 ROP experts for validation. The RSD was compared with the majority diagnosis of experts. The primary outcome measure was the percentage of accuracy of the i-ROP system classification of plus disease, with the RSD as a function of the field of view and vessel type. Secondary outcome measures included the accuracy of the 11 experts compared with the RSD. Accuracy of plus disease diagnosis by the i-ROP computer-based system was highest (95%; 95% CI, 94%-95%) when it incorporated vascular tortuosity from both arteries and veins and with the widest field of view (6-disc diameter radius). Accuracy was 90% or less when using only arterial tortuosity and 85% or less using a 2- to 3-disc diameter view similar to the standard published photograph. Diagnostic accuracy of the i-ROP system (95%) was comparable to that of 11 expert physicians (mean 87%, range 79%-99%). Experts in ROP appear to consider findings from beyond the posterior retina when diagnosing plus disease and consider tortuosity of both arteries and veins, in contrast with published definitions. It is feasible for a computer-based image analysis system to perform comparably with ROP experts, using manually segmented images.

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis.

We developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis. A dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the "i-ROP" system. Among the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%). This comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination. Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists.

Development of a Support Vector Machine - Based Image Analysis System for Focal Liver Lesions Classification in Magnetic Resonance Images

Purpose: The design and implementation of a computer-based image analysis system employing the support vector machine (SVM) classifier system for the classification of Focal Liver Lesions (FLLs) on routine non-enhanced, T2-weighted Magnetic Resonance (MR) images. Materials and Methods: The study comprised 92 patients; each one of them has undergone MRI performed on a Magnetom Concerto (Siemens). Typical signs on dynamic contrast-enhanced MRI and biopsies were employed towards a three class categorization of the 92 cases: 40-benign FLLs, 25-Hepatocellular Carcinomas (HCC) within Cirrhotic liver parenchyma and 27-liver metastases from Non-Cirrhotic liver. Prior to FLLs classification an automated lesion segmentation algorithm based on Marcov Random Fields was employed in order to acquire each FLL Region of Interest. 42 texture features derived from the gray-level histogram, co-occurrence and run-length matrices and 12 morphological features were obtained from each lesion. Stepwise multi-linear regression analysis was utilized to avoid feature redundancy leading to a feature subset that fed the multiclass SVM classifier designed for lesion classification. SVM System evaluation was performed by means of leave-one-out method and ROC analysis. Results: Maximum accuracy for all three classes (90.0%) was obtained by means of the Radial Basis Kernel Function and three textural features (Inverse- Different-Moment, Sum-Variance and Long-Run-Emphasis) that describe lesion's contrast, variability and shape complexity. Sensitivity values for the three classes were 92.5%, 81.5% and 96.2% respectively, whereas specificity values were 94.2%, 95.3% and 95.5%. The AUC value achieved for the selected subset was 0.89 with 0.81 - 0.94 confidence interval. Conclusion: The proposed SVM system exhibit promising results that could be utilized as a second opinion tool to the radiologist in order to decrease the time/cost of diagnosis and the need for patients to undergo invasive examination.

SU-D-134-06: Statistical Methods for Breast Mass Classification by Ultrasound Imaging

Purpose: The overall goal of this study is to develop a computer-based image analysis system for breast ultrasound that may aid physicians in differentiating benign and malignant masses with higher confidence and thus help in reducing unnecessary biopsies. Towards this goal we describe an approach that combines two independent probabilistic classifiers to improve diagnosis of breast masses. Methods: B-scan images of 266 patients with biopsy proven breast masses were analyzed for margin grayscale and shape features. These features were used with two statistical methods, logistic regression and naive Bayes, to classify the lesions as malignant and benign. The diagnostic performance of the ultrasound image features was evaluated by using them alone and by combining them with mammographic BI-RADS categories and patient age. The probability predictions of the two classifiers were compared to assess consensus between them. The performances of the classifiers were evaluated using area under the curve (AUC) of the ROC Receiver Operating Characteristic. Training and testing were performed using leave-one-out validation. Results: The results showed that combined features outperformed the individual features. Logistic regression performed slightly better than naive Bayes (AUC: 0.902 ± 0.023 vs. 0.865 ± 0.027, p < 0.03). The agreement between the two models at different decision thresholds was stable at ∼88 %. The reaming 12% were treated as the cases that needed further diagnostic investigation. Treating each algorithm as an independent observer and using the consensus between the two models as the criterion for mass differentiation demonstrated a reduction in biopsy by 48% could be achieved at the cost of missed malignancies in 2% cases. Conclusion: Computer-based image analysis of breast ultrasound images can aid the differentiation of benign and malignant masses and help in improving biopsy yields. NIH grant R01 CA130946

OP091: Angiogenin expression in tongue carcinoma: Any role in neo-angiogenesis and cell proliferation? A pilot study

Despite recent advances in diagnostic and therapeutic techniques, the prognosis for patients with head and neck squamous cell carcinoma (HNSCC) has not improved significantly. Novel clinical strategies are needed to identify HNSCC patients with a poor prognosis, who may benefit from a tailored therapy. Several bio-markers have been recently considered for prognostic purposes in oral carcinoma. Angiogenin (ANG) is a member of the ribonuclease superfamily and of medical interest largely because it supports the growth of primary and metastatic malignancies. ANG has recently been found involved in cancer cell proliferation as well as in tumor angiogenesis. The present study is the first to investigate the potential role of ANG in tongue carcinoma neo-angiogenesis and cancer cell proliferation. ANG expression (in carcinoma cells and endothelial intratumor vessel cells), CD105-assessed micro-vessel density (MVD), and MIB-1 expression were correlated with prognostic parameters in 28 consecutively-operated pT1–T2 tongue carcinomas (SCC). Whenever feasible, a computer-based image analysis system was used for the immunohistochemical reaction analysis. No significant correlations emerged between ANG expression in the tongue carcinoma cells or endothelial intratumor vessel cells and tongue carcinoma recurrence or disease-free survival (DFS). ANG expression was also unrelated to CD105-assessed MVD or MIB-1 expression. Conversely, CD105-assessed MVD correlated directly with recurrence rate ( p = 0.02), and DFS was significantly shorter in cases with CD105-assessed MVD > 167 micro-vessels/mm 2 than in those with MVD ⩽ 167 micro-vessels/mm 2 (p = 0.042). Our results support the hypothesis that CD105-assessed MVD would be a valuable parameter for predicting which patients with tongue SCC are at greatest risk of carcinoma recurrence. Despite our study results, the role of ANG in tongue carcinoma warrants further investigation in larger series because blocking ANG expression might have the potential not only to reduce angiogenesis in malignancies, but also to directly inhibit tumor growth.

Application of Hybrid Vision Method for the Hot Aluminium Surface Inspection

The paper presents the possibilities of a hybrid vision method based on simultaneous analysis of infrared and vision images for surface inspection of hot aluminium in a manufacturing process. The system consists of a NIR/SWIR camera and a high resolution visual camera, and a computer based image analysis system. The simultaneous analysis of infrared and vision images will enable surface inspection for detecting defects in temperature range from 200°C to 600°C. Thermal images present temperature distribution on the surface, and contain information about the manufacturing process. The analysis of thermograms enables to find areas of temperature irregularity caused by increased friction loads, as well as areas of inhomogeneous emissivity caused by surface defects. Furthermore, information captured by the vision camera is used to detect surface defects. The software developed enables the overlaying of images. The proposed simultaneous thermovision and vision imaging can be applied in industry for in-line monitoring of aluminium extrusion processes.

The role of angiogenin in pT1‐T2 tongue carcinoma neo‐angiogenesis and cell proliferation: an exploratory study

Angiogenin (ANG) is a member of the ribonuclease superfamily and of medical interest largely because it supports the growth of primary and metastatic malignancies. This study is the first to investigate the potential role of ANG in tongue carcinoma neo-angiogenesis and cancer cell proliferation. Angiogenin expression (in carcinoma cells and endothelial intratumor vessel cells), CD105-assessed micro-vessel density (MVD), and MIB-1 expression were correlated with prognostic parameters in 28 primarily consecutively operated pT1-T2 tongue carcinomas (squamous cell carcinoma [SCC]). Whenever feasible, a computer-based image analysis system was used for the immunohistochemical reaction analysis. No significant correlations emerged between ANG expression in the tongue carcinoma cells or endothelial intratumor vessel cells and tongue SCC recurrence rate or disease-free survival (DFS). ANG expression was also unrelated to CD105-assessed MVD or MIB-1 expression. Conversely, CD105-assessed MVD correlated directly with recurrence rate (P=0.02) and DFS was significantly shorter in cases with CD105-assessed MVD >167micro-vessels/mm(2) than in those with CD105-assessed MVD ≤167micro-vessels/mm(2) (P=0.042). Our results support the hypothesis that CD105-assessed MVD would be a valuable parameter for predicting which patients with tongue SCC are at greatest risk of disease recurrence. Despite our study results, the role of ANG in tongue carcinoma warrants further investigation in larger series.

Temporary morphological changes in plus disease induced during contact digital imaging

To compare and quantify the retinal vascular changes induced by non-intentional pressure contact by digital handheld camera during retinopathy of prematurity (ROP) imaging by means of a computer-based image analysis system, Retinal Image multiScale Analysis. A set of 10 wide-angle retinal pairs of photographs per patient, who underwent routine ROP examinations, was measured. Vascular trees were matched between 'compression artifact' (absence of the vascular column at the optic nerve) and 'not compression artifact' conditions. Parameters were analyzed using a two-level linear model for each individual parameter for arterioles and venules separately: integrated curvature (IC), diameter (d), and tortuosity index (TI). Images affected with compression artifact showed significant vascular d (P<0.01) changes in both arteries and veins, as well as in artery IC (P<0.05). Vascular TI remained unchanged in both groups. Non-adverted corneal pressure with the RetCam lens could compress and decrease intra-arterial diameter or even collapse retinal vessels. Careful attention to technique is essential to avoid absence of the arterial blood column at the optic nerve head that is indicative of increased pressure during imaging.

Reliability of magnetic resonance imaging measurements of the cross-sectional area of the muscle contractile and non-contractile components

Magnetic resonance imaging (MRI) is increasingly used to study skeletal muscles patients with muscular disorders. We report an MRI technique for evaluating the trunk muscles. This technique takes both the component surface area (CSA) and the density of the muscles on MRI axial slices into account . Using a computer-based image analysis system, we combined MRI data measuring the muscle CSA, which was separated into the contractile component (CCSA) and the non-contractile component (NCCSA). The purpose of this study was to analyze the reliability of this method of measuring the CSA, CCSA and NCCSAin trunk muscles on MRI axial slices through L4 and T12. Thirty volunteer subjects were enrolled in this study. Two acquisitions were performed. For the reliability analyses, each of the two slices (T12 and L4) from 30 subjects was measured by three raters trained in this technique, on two occasions 2weeks apart. Each muscle was surrounded and its CSA, NCCSA and CCSA were recorded. For each muscle, the agreement between the two sets of 30 measurements performed by three observers was evaluated by calculating an intra-class correlation coefficient (ICC). Regarding the slice through L4 and T12, the reliabilities of the measurement of CSA, CCSA were very good for all the muscles except the parietal muscles. MRI measurements of the trunk muscle cross-sectional areas and of the CCSA and NCCSA are reliable.

Classification of Papulo‐Squamous skin diseases using image analysis

Papulo-squamous skin diseases are variable but are very close in their clinical features. They present with the same lesions, erythematous scaly lesions. Clinical evaluation of skin lesions is based on common sense and experience of the dermatologist to differentiate features of each disease. To evaluate a computer-based image analysis system as a helping tool for classification of commonly encountered diseases. The study included 50 selected images from each of psoriasis, lichen planus, atopic dermatitis, seborrheic dermatitis, pityrasis rosea, and pitryasis rubra pilaris with a total of 300 images. The study comprised three main processes peformed on the 300 included images: segmentation, feature extraction followed by classification. Rough sets recorded the highest percentage of accuracy and sensitivity of segmentation for the six groups of diseases compared with the other three used techniques (topological derivative, K-means clustering, and watershed). Rule-based classifier using the concept of rough sets recorded the best percentage of classification (96.7%) for the six groups of diseases compared with the other six techniques of classification used: K-means clustering, fuzzy c-means clustering, classification and regression tree, rule-based classifier with discretization, and K-nearest neighbor technique. Rough sets approach proves its superiority for both the segmentation and the classification processes of papulo-squamous skin diseases compared with the other used segmentation and classification techniques.

Neoangiogenesis in laryngeal carcinoma: angiogenin and CD105 expression is related to carcinoma recurrence rate and disease‐free survival

Angiogenin regulates angiogenesis supporting primary and metastatic tumour growth. CD105 is a proliferation-associated protein expressed in angiogenic endothelial cells. The aim of this study was to investigate for the first time angiogenin expression in laryngeal squamous cell carcinoma (LSCC) and to evaluate the relationship between angiogenin and CD105 expression, clinicopathological and prognostic parameters. A total of 108 consecutive operable LSCCs were studied. Angiogenin expression was determined immunohistochemically in both carcinoma cells and intratumoral vessels. CD105 expression was evaluated in endothelial cells of LSCC and calculated by a computer-based image analysis system. The percentage of the fields occupied by CD105-assessed microvessels was determined. Angiogenin expression in carcinoma cells was higher in LSCC patients who experienced loco-regional recurrence of disease (P=0.032). Disease-free survival (DFS) was shorter in cases with carcinoma cells showing angiogenin expression >21.0% (P=0.035). Angiogenin expression in carcinoma cells was correlated strongly with the angiogenin score in endothelial cells of intratumoral vessels (P=0.0000). Higher loco-regional carcinoma recurrence rate and shorter DFS in patients with high CD105 expression were found (P=0.0004, P=0.0001). Angiogenin expression in laryngeal carcinoma cells and CD105 expression can be considered as potentially useful to detect LSCC patients with higher risk of disease recurrence who might benefit from more aggressive therapy.