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In this paper, we propose a method for classification of histopathological images using texture features. The images are first segmented as epithelial lining surrounding the lumen for breast histopathology images using spatio-color-texture graph segmentation method. The features such as Gray Level Co-occurrence Matrix (GLCM), Graph Run Length Matrix (GRLM) features, and Euler number are extracted...
Prostate cancer is considered to be one of the main causes of cancer related death for men in the United States. Automated methods for prostate cancer localization based on multispectral magnetic resonance imaging (MRI) haver recently emerged as a non invasive technique for this purpose as an alternative to transrectal ultrasound. However, the automated methods developed to this date require a manual...
Histologic analysis of a stained tissue sample by a trained pathologist forms the definitive diagnosis of prostate cancer. Rapid and objective second opinions are highly desirable to make more accurate diagnostic decisions. One alternate method is to use Fourier transform infrared (FT-IR) spectroscopic imaging, which is an emerging technique that combines the molecular selectivity of spectroscopy...
Acute lymphoblastic leukemia (ALL) are a group of hematological neoplasia of childhood which is characterized by a large number of lymphoid blasts in the blood stream. ALL makes around 80% of childhood leukemia and it mostly occur in the age group of 3-7. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed due to imitation of similar...
In this paper, we present a fully automated method for cell nuclei detection in Pap smear images. The locations of the candidate nuclei centroids in the image are detected with morphological analysis and they are refined in a second step, which incorporates a priori knowledge about the circumference of each nucleus. The elimination of the undesirable artifacts is achieved in two steps: the application...
Acute lymphoblastic leukemia (ALL) is the most common hematological neoplasia of childhood and is characterized by uncontrolled growth of leukemic cells in bone marrow, lymphoid organs etc. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed due to imitation of similar signs by other disorders. Careful microscopic examination of...
Cervical cancer is one of the deadliest cancer known and is also a key research area in image processing. The main problem with this cancer is that it cannot be detected as it doesn't throw any symptoms until the final stages. This is attributed to the cancer itself and also to the lack of pathologists available to screen the cancer. Here we have proposed a novel approach to classify the various malignancies...
The characterization and quantitative description of histological images is not a simple problem. To reach a final diagnosis, usually the specialist relies on the analysis of characteristics easily observed, such as cells size, shape, staining and texture, but also depends on the hidden information of tissue localization, physiological and pathological mechanisms, clinical aspects, or other etiological...
The key step of a computer-assisted screening system that aims early diagnosis of cervical cancer is the accurate segmentation of cells. In this paper, we propose a two-phase approach to cell segmentation in Pap smear test images with the challenges of inconsistent staining, poor contrast, and overlapping cells. The first phase consists of segmenting an image by a non-parametric hierarchical segmentation...
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...
HER-2/neu, a protein often giving higher aggressiveness in breast cancers, has been shown that if the gene is expanded for some reason, the Her-2 protein produced by the cells will be over-expressed to enhance the cancer cells reproduced ability, the prognosis will be also relatively less, too. The HER-2 immunohistochemical stained provides a simple and reliable method for pathologist in clinical...
Quantitative analysis of brain lesions in large clinical trials is becoming more and more important. We present a new automated method, that combines intensity based lesion segmentation with a false positive elimination method based on the spatial distribution of lesions. A Support Vector Regressor (SVR) is trained on expert-defined lesion masks using image histograms as features, in order to obtain...
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...
Breast cancer is the most common cancer diagnosed among U.S. women. In this paper we have done some experiments for tumor detection in digital mammogram images. First of all, we have described a method that segments the breast image automatically. As a preprocessing, we have used fuzzy based noise removal filter that removes noise. Then for segmentation, we have provided a background removal method...
The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed...
Color chromosome classification (karyotyping) allows simultaneous analysis of numerical and structural chromosome abnormalities. The success of the technique largely depends on the accuracy of pixel classification. In this paper we present a method for multichannel chromosome image classification based on support vector machines. First, the image is segmented using a multichannel watershed segmentation...
Mass in mammogram can be an indicator of breast cancer. In this work we propose a new approach using twin support vector machine (TWSVM) for automated detection of mass in digital mammograms. This algorithm finds two hyperplanes to classify data points into different classes according to the relevance between a given point and either plane. It works much faster than original SVM classifier. The proposed...
Triple-negative (TN) breast cancer has gained much interest recently due to its lack of response to receptor-targeted therapies and its aggressive clinical nature. In this study, we evaluate the ability of a computer-aided diagnosis (CAD) system to not only distinguish benign from malignant lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), but also to quantitatively distinguish...
The current gold standard for predicting disease survival and outcome for lymph node-negative, estrogen receptor-positive breast cancer (LN-, ER+ BC) patients is via the gene-expression based assay, Oncotype DX. In this paper, we present a novel computer-aided prognosis (CAP) scheme that employs quantitatively derived image information to predict patient outcome analogous to the Oncotype DX recurrence...
Confocal reflectance microscopy is an emerging modality, for dermatology applications, especially for in-situ and bedside detection of skin cancers. As this technology gains acceptance, automated processing methods become increasingly important to develop. Since the dominant internal feature of the skin is the epidermis/dermis boundary, it has been chosen as the initial target for this development...
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