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Features extracted from cell networks have become popular tools in histological image analysis. However, existing features do not take sufficient advantage of the cycle structure present within the cell networks. We introduce a new class of network cycle features that take advantage of such structures. We demonstrate the utility of these features for automated prostate cancer scoring using histological...
Breast cancer is reported to be the second deadliest cancer among cancerous woman. Statistics show that the case of breast cancer in the world is increasing every year. By analyzing a mammogram, pathologists could detect the presence of micro calcification in ones breast. However, micro calcification could be classified into benign and malignant. The later indicates the presence of cancer. Computer-Aided...
In this paper, we investigate the classification of masses with texture features. We propose an improved level set method to find the boundary of a mass, based on the initial contour provided by radiologists. After the boundary of a mass is found, texture features from Gray Level Co-occurrence Matrix (GLCM) are extracted from the surrounding area of the boundary of the mass. The extracted texture...
Color variation in medical images degrades the classification performance of computer aided diagnosis systems. Traditionally, color segmentation algorithms mitigate this variability and improve performance. However, consistent and robust segmentation remains an open research problem. In this study, we avoid the tenuous phase of color segmentation by adapting a bag-of-features approach using scale...
Recognition of prostate calculus is an important step to determine the source of pathological organ, and is of great importance for further diagnosis of prostate cancer. In this paper, due to some tissues are similar to calculus, and prostate calculus usually adheres to other tissues, a recognition algorithm for prostate calculus based on transition region and PCA-SVM is proposed. Firstly, local entropy,...
Prostate cancer is one of the leading causes of cancer death for men. However, early detection before cancer spreads beyond the prostate can reduce the mortality. Therefore, in vivo imaging techniques play an important role to localize the prostate cancer for treatment. Although magnetic resonance imaging (MRI) has been proposed to localize prostate cancer, the studies on automated localization with...
The objective of this study is to investigate the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. A computer-assisted classification method combining conventional magnetic resonance imaging (MRI) and perfusion MRI is developed and used for differential diagnosis. The proposed scheme...
Prostate cancer is a leading cause of cancer death for men in the United States. There is currently no widely adopted accurate noninvasive method for localizing prostate cancer using imaging. If such as technique were available it could be used to guide biopsy, radiotheraphy and surgery. However, current imaging techniques are limited due to inability to detect cancers, intensity changes related to...
Prostate cancer is one of the most frequent cancers caused in men and automated classification results which can be provided as objective references are of great significance. Here we present a study of classification of histological images of prostate based on both morphological features and textural features. At first we get two tissues of prostate cancer which including nuclei, lumen from the image,...
An automated method that detects early cancerous specimens based on image analysis is described. After acquisition and noise reduction, the microscope images are segmented into individual cell nucleus, from which the feature vectors of nucleus are calculated. The dimensionality of the feature vectors is then reduced using a method combing F-Score and random forest algorithms. The types of the cell...
DNA microarrays technology enables us to obtain information about expression levels of thousands of genes at the same time. This technology promises to monitor the whole genome on a single chip so that researchers can have a better picture of the interactions among thousands of genes at the same time. It becomes a challenge to extract information from the large amount of data through data mining....
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