Computer Image Analysis Techniques Research Articles

Selection of the Discriming Feature Using the BEMD’s BIMF for Classification of Breast Cancer Mammography Image

Mammogram exam images are useful in identifying diseases, such as breast cancer, which is one of the deadliest cancers, affecting adult women around the world. Computational image analysis and machine learning techniques can help experts identify abnormalities in these images. In this work we present a new system to help diagnose and analyze breast mammogram images. To do this, the system a method the Selection of the Most Discriminant Attributes of the images preprocessed by BEMD “SMDA-BEMD”, this entails picking the most pertinent traits from the collection of variables that characterize the state under study. A reduction of attribute based on a transformation of the data also called an extraction of characteristics by extracting the Haralick attributes from the Co-occurrence Matrices Methods “GLCM” this reduction which consists of replacing the initial set of data by a new reduced set, constructed at from the initial set of features extracted by images decomposed using Bidimensional Empirical Multimodal Decomposition “BEMD”, for discrimination of breast mammogram images (healthy and pathology) using BEMD. This decomposition makes it possible to decompose an image into several Bidimensional Intrinsic Mode Functions “BIMFs” modes and a residue. The results obtained show that mammographic images can be represented in a relatively short space by selecting the most discriminating features based on a supervised method where they can be differentiated with high reliability between healthy mammographic images and pathologies, However, certain aspects and findings demonstrate how successful the suggested strategy is to detect the tumor. A BEMD technique is used as preprocessing on mammographic images. This suggested methodology makes it possible to obtain consistent results and establishes the discrimination threshold for mammography images (healthy and pathological), the classification rate is improved (98.6%) compared to existing cutting-edge techniques in the field. This approach is tested and validated on mammographic medical images from the Kenitra-Morocco reproductive health reference center (CRSRKM) which contains breast mammographic images of normal and pathological cases.

A Novel Fractional Edge Detector Based on Generalized Fractional Operator

This work pioneers a novel approach in image edge detection through the utilization of the generalized fractional operator. By harnessing the global attributes inherent in fractional derivatives, it aims to enhance the extraction of intricate edge details.This is accomplished by creating the mask by using fractional derivative and adapt the mask by another parameter, yielding compelling and informative edge representations, as validated by experimental results. This advancement not only augments computer vision and image analysis techniques but also holds promise for refining image processing methodologies. Future endeavors may explore its adaptability across diverse imaging domains like medical and satellite imagery, while integration into deep learning frameworks could elevate its potential for advanced feature extraction and deeper image understanding. Additionally, optimizing its computational efficiency would broaden its scope for real-time deployment in fields such as robotics and autonomous systems.

Hierarchical packaging design image analysis based on multi-dimensional image fusion

Packaging is an important part of any product. This is the first thing customers see when they enter the store. It can achieve or destroy sales. Packaging plays an important role in the success of any product, but it must also attract consumers to make them want to buy it. The packaging method of products will affect their success, so designers need to consider this when designing products. Packaging is an important part of any product. This is the first thing customers notice when they enter the store. Packaging can achieve or destroy the sales of products. It is important to have effective packaging design to ensure that your products are protected during transportation and sales. In this article, I will discuss the importance of designing effective packaging for products and how to use computer image analysis techniques such as image fusion to correctly analyze images.

Computer vision system for research in the area of defectoscopy for materials and products

In many cases, visual and optical methods can be used in defectoscopy for different materials and products. With the development of microprocessor components and significant expansion of usage of computer technologies and image processing and analysis techniques in different areas, the use of visual and optical methods in defectoscopy for production and research purposes is rapidly developing. In this paper, the author proposes a computer vision system for experiments and research in the area of studying defects of materials and products. The system uses modern methods of image processing and object identification based on their images. The system allows to install the object so that it can be rotated horizontally, take high-quality images of the object using a digital video camera, pre- process images to enhance image quality using a local computing module, transfer images to the main computing module to identify defects and make decisions about rejection of the material or product. To install and rotate the material or product, the author uses the stepper motor 17HS4401 and a horizontal platform fixed on the vertical axis. The stepper motor is controlled using Microstep Driver TB6600 and a local computing module based on a microcontroller with an ARM Cortex-M7 core. The video stream is recorded using a USB microscope video camera which provides sufficiently high image resolution allowing to find defects on the object surface of size 50 micron and larger. Rotation speed can be controlled using a local computing module. The input data for the local computing module can be provided in the form of a video stream or a sequence of images. The local computing module has an LCD screen based on the ВС1602А indicator, programmable LEDs, a keyboard to select operating modes for the stepper motor, a USB port to connect the microscope video camera and an SWD port to program the Flash memory and debug the firmware in real time. Original images or the images after quality enhancement are passed to the main computing module using the SPI interface. The author has developed software for the local computing module to control the stepper motor, record a video stream or series of images of the object area with possible defects, quality enhancement and passing the video stream or images to the main computing module for further processing and analysis. The results can be used in scientific research and in development of automated systems for non-destructive defectoscopy for materials and end products.

A quantitative study of bone defects in displaced femoral neck fractures based on virtual reduction techniques

BackgroundBone defects in femoral neck fractures are strongly associated with the prognosis after internal fixation. However, qualitative analysis of bone defects in femoral neck fractures has already been performed, quantitative studies have not been reported. In this study, we aimed to systematically analyse the morphological characteristics of bone defects in patients with femoral neck fractures using computed tomography (CT) images combined with computer image analysis techniques. MethodsFour hundred and sixty-nine patients with femoral neck fractures from January 2014 to December 2018 at two grade A tertiary hospitals were included. Models were created in Mimics software based on CT images collected within 1 week after injury and then imported into 3-matic software for virtual reduction. The volume of the bone defect (VBD), maximum defect thickness (MDT), extent of the bone defect region (EBDR) , main defect quadrant (MDQ), collapse type and fracture classification were calculated and recorded. ResultsThe EBDR, collapse type and MDT all had a significant positive effect on the VBD (P <0.05), with a more significant effect at higher quantiles. Age also had a significant positive effect on the VBD (P < 0.05), but its effect was more pronounced at lower quantiles. Compared to non-subcapital fractures, subcapital fractures had a positive effect on the VBD only at the 50 and 75% quantiles (P < 0.01). The female sex had a significant negative effect on the VBD compared to the male sex (P < 0.05). ConclusionThis study established a reliable computer image processing method for quantitative analysis of the VBD in femoral neck fractures and revealed that all patients with femoral neck fractures had bone defects, which can occur at any part of the femoral neck. The EBDR, MDT, collapse type, and patient age and sex were all important risk factors for the extent of the defect and should be taken into account in surgical planning.

Meta-learning Based Prediction of Different Corn Cultivars from Colour Feature Extraction with Image Processing Technique

Image analysis techniques are developing as applicable to the approaches of quantitative analysis, which is aimed to determine cultivar grains. Additionally, corn (Zea mays) grain processing companies evaluate the quality of kernels to determine the price of these cultivars. Because of this reason, in the study, a computer image analysis technique was applied on three corn cultivars. These were Zea mays L. indentata, Zea mays L. saccharata and a hybrid corn (Yellow sweet corn). These cultivars are commercially important as dry grains in Turkey. In the study, the grain color values were tested in the cultivars from Turkey’s collection. One hundred samples were used for each corn cultivar, and 300 corn grains in total were used for evaluations. Each of nine color parameters (Rmin, Rmean, Rmax, Gmin, Gmean, Gmax, Bmin, Bmean, Bmax) which were obtained from original RGB color channels with maximum and minimum values was evaluated from the digital images of three different corn cultivar grains. The values were analyzed with the help of the Multilayer Perceptron (MLP), Decision Tree (DT), Gradient Boost Decision Tree (GBDT) and Random Forest (RF) algorithms by using the Knime Analytics Platform. The majority voting method was applied to MLP and DT for prediction fusion. All algorithms were run with a 10-fold cross-validation method. The success of prediction accuracy was found as 99% for RF and GBDT, 97.66% for MLP, 96.66% DT and 97.40% for Majority Voting (MAVL). The MAVL method increased the accuracy of DT while decreasing the accuracy of MLP partly for the fusion of MLP and DT.

Cryo-EM in drug discovery: achievements, limitations and prospects.

Cryo-electron microscopy (cryo-EM) of non-crystalline single particles is a biophysical technique that can be used to determine the structure of biological macromolecules and assemblies. Historically, its potential for application in drug discovery has been heavily limited by two issues: the minimum size of the structures it can be used to study and the resolution of the images. However, recent technological advances - including the development of direct electron detectors and more effective computational image analysis techniques - are revolutionizing the utility of cryo-EM, leading to a burst of high-resolution structures of large macromolecular assemblies. These advances have raised hopes that single-particle cryo-EM might soon become an important tool for drug discovery, particularly if they could enable structural determination for 'intractable' targets that are still not accessible to X-ray crystallographic analysis. This article describes the recent advances in the field and critically assesses their relevance for drug discovery as well as discussing at what stages of the drug discovery pipeline cryo-EM can be useful today and what to expect in the near future.

Evaluation of nuclear chromatin using grayscale intensity and thresholded percentage area in liquid-based cervical cytology.

Development of computerized image analysis techniques has opened up the possibility for the quantitative analysis of nuclear chromatin in pathology. We hypothesized that the features extracted from digital images could be used to determine specific cytomorphological findings for nuclear chromatin that may be applicable for establishing a medical diagnosis. Three parameters were evaluated from nuclear chromatin images obtained from the liquid-based cervical cytology samples of patients with biopsy-proven high-grade squamous intraepithelial lesion (HGSIL), and compared between non-neoplastic squamous epithelia and dysplastic epithelia groups: (1) standard deviation (SD) of the grayscale intensity; (2) difference between the maximum and minimum grayscale intensity (M-M); and (3) thresholded area percentage. Each parameter was evaluated at the mean, mean-1SD, and mean-2SD thresholding intensity levels. Between the mean and mean-1SD levels, the thresholded nuclear chromatin pattern was most similar to the chromatin granularity of the unthresholded grayscale images. The SD of the gray intensity and the thresholded area percentage differed significantly between the non-neoplastic squamous epithelia and dysplastic epithelia of HGSIL images at all three thresholding intensity levels (mean, mean-1SD, and mean-2SD). However, the M-M significantly differed between the two sample types for only two of the thresholding intensity levels (mean-1SD and mean-2SD). The digital parameters SD and M-M of the grayscale intensity, along with the thresholded area percentage could be useful in automated cytological evaluations. Further studies are needed to identify more valuable parameters for clinical application.

Susceptibility of sweet and sour cherry cultivars/genotypes to feeding damage caused by Bryobia rubrioculus (Acari: Tetranychidae)

The sweet cherry Prunus avium L. and sour cherry Prunus cerasus L. are considered important fruit trees, providing valuable and delicious fruit worldwide. We compared the susceptibility of 10 cultivars of sweet cherry and 5 genotypes of sour cherry to Bryobia rubrioculus Scheuten feeding by manually counting brown spots and by measuring damaged leaf area using a computerized image analysis technique. Damaged leaves were individually scanned on a common flatbed color scanner on a white background. The cultivars and genotypes of sweet and sour cherries, based on the mean area percentage having feeding symptoms on the leaves, were classified into three groups: (1) "susceptible", i.e., Hamedan, BN 5150, BT 5148, Zard-90, KB 9 and KB 10; (2) "semi-resistant", i.e., KB 21, Sabima, BT 5124, KB 25 and Lambert; and (3) "resistant", i.e., Haj Yousefi, BO 5187, BT 5154 and Siah Mashhad. A corresponding increase in the mean value of the symptom area was observed as the number of adult brown mites increased per disc. The results of both methods were compared, and the advantages of each method were discussed.

Optimal selection of features using wavelet fractal descriptors and automatic correlation bias reduction for classifying skin lesions

Abstract The non-invasive computerized image analysis techniques have a great impact on accurate and uniform evaluation of skin abnormalities. The paper reports a method for the texture and morphological feature extraction from skin lesion images to differentiate common melanoma from benign nevi. In this work, a 2D wavelet packet decomposition (WPD) based fractal texture analysis has been proposed to extract the irregular texture pattern of the skin lesion area. On the whole 6214 features have been extracted from each of the 4094 skin lesion images, by analyzing the textural pattern and morphological structure of the lesion area. For the identification of the most efficient feature set, an improved correlation bias reduction method has been introduced in combination with support vector machine recursive feature elimination (SVM-RFE). An automatic selection of correlation threshold value has been introduced in this proposed work to eliminate the correlation bias problem associated with SVM-RFE algorithm. With these selected features, the support vector machine (SVM) classifier with radial basis function is found to achieve the classification performance of 97.63% sensitivity, 100% specificity and 98.28% identification accuracy. The results show that the scheme presented in this paper surpasses the performance of the other state-of-the art techniques for the differentiation of melanoma from other skin abnormalities.

Wheat landraces identification through glumes image analysis

A new practical method able to identify wheat local landraces was implemented. It is based on computerized image analysis techniques and statistical identification, for the first time on the basis of glumes size, shape, colour and texture.Ears of 52 different Sicilian wheat landraces were reaped for three consecutive years. Digital images of the glumes were acquired, processed and analysed, measuring 138 quantitative morpho-colorimetic variables. The data were statistically analysed applying a Linear Discriminant Analysis. All the statistical comparisons, distinguished for systematic rank, given perfect identification performances; while an overall percentage of correct identification of 89.7% was reached when all the landraces were compared all together.Finally, the identification system was tested with an unknown glume sample, later entirely identified as Vallelunga, one of the Sicilian landraces.This work represents the first attempt of wheat landraces identification based on glume phenotypic characters, applying image analysis techniques. Considering the growing interest in local old wheat landraces, strongly linked to the renewed appreciation in traditional and typical local products, the obtained results support the application of the image analysis system not only for grading purposes, but also to define the product traceability, in order to get a “market card” for wheat landraces.

Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework

Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive medical tests to define the full range of phenotypic variation associated with individual health. Such knowledge is critical for improved early intervention, for better treatment decisions, and for ameliorating the steadily worsening epidemic of chronic disease. We present proof-of-concept experiments to demonstrate how routinely acquired cross-sectional CT imaging may be used to predict patient longevity as a proxy for overall individual health and disease status using computer image analysis techniques. Despite the limitations of a modest dataset and the use of off-the-shelf machine learning methods, our results are comparable to previous ‘manual’ clinical methods for longevity prediction. This work demonstrates that radiomics techniques can be used to extract biomarkers relevant to one of the most widely used outcomes in epidemiological and clinical research – mortality, and that deep learning with convolutional neural networks can be usefully applied to radiomics research. Computer image analysis applied to routinely collected medical images offers substantial potential to enhance precision medicine initiatives.

Three-dimensional analysis of tarsal bone response to axial loading in patients with hallux valgus and normal feet

BackgroundPatients with hallux valgus present a variety of symptoms that may be related to the type of deformity. Weightbearing affects the deformities, and the evaluation of the load response of tarsal bones has been mainly performed using two-dimensional plane radiography. The purpose of this study was to investigate and compare structural changes in the medial foot arch between patients with hallux valgus and normal controls using a computer image analysis technique and weightbearing computed tomography data. MethodsEleven patients with hallux valgus and eleven normal controls were included. Computed tomograms were obtained with and without simulated weightbearing using a compression device. Computed tomography data were transferred into a personal computer, and a three-dimensional bone model was created using image analysis software. The load responses of each tarsal bone in the medial foot arch were measured three-dimensionally and statistically compared between the two groups. FindingsDisplacement of each tarsal bone under two weightbearing conditions was visually observed by creating three-dimensional bone models. At the first metatarsophalangeal joint, the proximal phalanges of the hallux valgus group showed significantly different displacements in multiple directions. Moreover, opposite responses to axial loading were also observed in both translation and rotation between the two groups. InterpretationWeightbearing caused deterioration of the hallux valgus deformity three-dimensionally at the first metatarsophalangeal joint. Information from the computer image analysis was useful for understanding details of the pathology of foot disorders related to the deformities or instability and may contribute to the development of effective conservative and surgical treatments.

Prototype system for feature extraction, classification and study of medical images

Colonoscopy exam images are useful to identify diseases, such as the colorectal cancer, which is one of the most common cancers worldwide. Computational image analysis and machine learning techniques can assist experts to identify abnormalities in these images. In this work, we present and evaluate MIAS 3.0, which aims to help experts to study and analyze colon tissue images. To do so, the system initially extracts features from these images. Currently, Amadasum, Haralick and Laws texture descriptors are supported. Then, the described images are classified into normal or abnormal images. In this version, J48, nearest neighbor, backpropagation based on multilayer perceptron, naive Bayes, and support vector machine classification algorithms are implemented. MIAS was developed with open source technologies using a software engineering approach to improve flexibility and maintainability. In this work, MIAS was quantitatively assessed by its application in a set of 134 tissue image fragments. The classifiers built from this set were compared according to the cross-validation and contingency table strategies. Also, the system was qualitatively evaluated using 12 heuristics by twelve volunteers from Health and Exact Sciences. The issues found were categorized according to Rolf Molich’s severity scale. As a result, the J48 classifier achieved the highest sensitivity (85.07%) and reasonable average error (18.68%). In the qualitative evaluation, 61.26% of the issues found were not considered serious. These assessments suggest that MIAS can be useful to assist domain experts with minimum knowledge in informatics to conduct more complete studies of medical images, by identifying patterns regarding different abnormalities.

Analysis of surface metal fiber arrangement of electromagnetic shielding fabric and its influence on shielding effectiveness

Purpose No adequate study on scientific analysis of surface metal fiber (SMF) arrangement of electromagnetic shielding fabric (EMSF) and influence on shielding effectiveness (SE) is available at present. Design/methodology/approach This paper recognizes the SMF region and constructs a binary feature matrix according to edge condition, width condition and gray condition using the computer image analysis technique based on the construction of the surface digitized image of the EMSF. Three parameters of coverage, dispersion and uniformity are proposed to describe the SMF arrangement. Then experiments and testing samples are designed to analyze the relationship between the three parameters and the SE. Findings Results show that the proposed method can accurately recognize the SMF of the EMSF, the coverage, dispersion and uniformity can describe three aspects of the SMF arrangement of percentage content, porosity and orientation, and the three parameters are positively, negatively and positively correlated to the SE, respectively. Originality/value The research in this paper provides the basis for further description of the SMF arrangement of the EMSF, possesses the significance for the study of the shielding mechanism, transmission model, electromagnetic performance and rapid non-destructive evaluation of the EMSF and provide a new idea for the study on the shielding theory and application of the EMSF.

Inner membrane fusion mediates spatial distribution of axonal mitochondria.

In eukaryotic cells, mitochondria form a dynamic interconnected network to respond to changing needs at different subcellular locations. A fundamental yet unanswered question regarding this network is whether, and if so how, local fusion and fission of individual mitochondria affect their global distribution. To address this question, we developed high-resolution computational image analysis techniques to examine the relations between mitochondrial fusion/fission and spatial distribution within the axon of Drosophila larval neurons. We found that stationary and moving mitochondria underwent fusion and fission regularly but followed different spatial distribution patterns and exhibited different morphology. Disruption of inner membrane fusion by knockdown of dOpa1, Drosophila Optic Atrophy 1, not only increased the spatial density of stationary and moving mitochondria but also changed their spatial distributions and morphology differentially. Knockdown of dOpa1 also impaired axonal transport of mitochondria. But the changed spatial distributions of mitochondria resulted primarily from disruption of inner membrane fusion because knockdown of Milton, a mitochondrial kinesin-1 adapter, caused similar transport velocity impairment but different spatial distributions. Together, our data reveals that stationary mitochondria within the axon interconnect with moving mitochondria through fusion and fission and that local inner membrane fusion between individual mitochondria mediates their global distribution.

SU‐E‐J‐275: Review ‐ Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy

Purpose:To review the literature in using computerized PET/CT image analysis for the evaluation of tumor response to therapy.Methods:We reviewed and summarized more than 100 papers that used computerized image analysis techniques for the evaluation of tumor response with PET/CT. This review mainly covered four aspects: image registration, tumor segmentation, image feature extraction, and response evaluation.Results:Although rigid image registration is straightforward, it has been shown to achieve good alignment between baseline and evaluation scans. Deformable image registration has been shown to improve the alignment when complex deformable distortions occur due to tumor shrinkage, weight loss or gain, and motion. Many semi‐automatic tumor segmentation methods have been developed on PET. A comparative study revealed benefits of high levels of user interaction with simultaneous visualization of CT images and PET gradients. On CT, semi‐automatic methods have been developed for only tumors that show marked difference in CT attenuation between the tumor and the surrounding normal tissues. Quite a few multi‐modality segmentation methods have been shown to improve accuracy compared to single‐modality algorithms. Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features have been found more informative than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Predictive models based on machine learning technique have been constructed for correlating selected image features to response. These models showed improved performance compared to current methods using cutoff value of a single measurement for tumor response.Conclusion:This review showed that computerized PET/CT image analysis holds great potential to improve the accuracy in evaluation of tumor response.This work was supported in part by the National Cancer Institute Grant R01CA172638.

Application of stereology in engineering of building materials

The article presents the literature review about the application of stereology and image analysis for quantitative evaluation of the building materials structure. At the outset, the development of stereological methods and computer image analysis techniques in the study of building materials was provided. Then quantitative structure parameters were defined and their methods of determining were showed. In the paper, the application of image analysis for the determination of properties of the cement composites was reported, including: an assessment of the porosity of the hardened concrete, determination of the aggregate distribution in the cementitious matrix, the crack analysis. It was found that the leading problem of image analysis is the process of sample preparation in order to obtain the correct extraction of examinated phase, and measurement automation process.

Computerized characterization of lung nodule subtlety using thoracic CT images

The goal of this work is to design computerized image analysis techniques for automatically characterizing lung nodule subtlety in CT images. Automated subtlety estimation methods may help in computer-aided detection (CAD) assessment by quantifying dataset difficulty and facilitating comparisons among different CAD algorithms. A dataset containing 813 nodules from 499 patients was obtained from the Lung Image Database Consortium. Each nodule was evaluated by four radiologists regarding nodule subtlety using a 5-point rating scale (1: most subtle). We developed a 3D technique for segmenting lung nodules using a prespecified initial ROI. Texture and morphological features were automatically extracted from the segmented nodules and their margins. The dataset was partitioned into trainers and testers using a 1:1 ratio. An artificial neural network (ANN) was trained with average reader subtlety scores as the reference. Effective features for characterizing nodule subtlety were selected based on the training set using the ANN and a stepwise feature selection method. The performance of the classifier was evaluated using prediction probability (PK) as an agreement measure, which is considered a generalization of the area under the receiver operating characteristic curve when the reference standard is multi-level. Using an ANN classifier trained with a set of 2 features (selected from a total of 30 features), including compactness and average gray value, the test concordance between computer scores and the average reader scores was 0.789 ± 0.014. Our results show that the proposed method had strong agreement with the average of subtlety scores provided by radiologists.

An effective hair removal algorithm for dermoscopy images

Dermoscopy is one of the major imaging modalities used in the diagnosis of pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized image analysis techniques have become important tools in this research area. Hair removal from skin lesion images is one of the key problems for the precise segmentation and analysis of the skin lesions. In this study, we present a new scheme that automatically detects and removes hairs from dermoscopy images. The proposed algorithm includes two steps: firstly, light and dark hairs and ruler marking are segmented through adaptive canny edge detector and refinement by morphological operators. Secondly, the hairs are repaired based on multi-resolution coherence transport inpainting. The algorithm was applied to 50 dermoscopy images. To estimate the accuracy of the proposed hair detection algorithm, quantitative analysis was performed using TDR, FPR, and DA metrics. Moreover, to evaluate the performance of the proposed hair repaired algorithm, three statistical metrics namely entropy, standard deviation, and co-occurrence matrix were used. The results demonstrate that the proposed algorithm is highly accurate and able to detect and repair the hair pixels with few errors. In addition, the segmentation veracity of the skin lesion is effectively improved after our proposed hair removal algorithm.