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In this paper we have presented an automated diagnosis of breast cell cancer using histopathological images on the basis of different textural descriptors. In the proposed technique, the images being preprocessed using extended adaptive-top-bottom transform (EAHE-TBhat) and segmented the nuclei regions from the non-nuclei regions using region growing segmentation. The nuclei regions are then used...
Breast cancer is one of the major causes of death among women around the world. To diagnose this disease using mammography technique, segmentation is an important step to detect the suspicious region(s) of mammograms. Segmentation concerns to the process of division of mammograms into different sections. Objective of segmentation is to simply modify the presentation of an image so that it becomes...
A prime factor deciding the survival rate of a breast cancer patient is the accuracy with which the malignancy grade of a breast tumor is determined. A Fine Needle Aspiration (FNA) biopsy is a key mechanism for breast cancer diagnosis as well as for assigning grades to malignant cases. In this paper, based on published cytological malignancy grading systems, we propose six computer-aided grading frameworks...
Computer-aided diagnosis systems (CADx) play a major role in the early diagnosis of breast cancer. Extracting the breast region precisely from a mammogram is an essential component of CADx for mammography. The appearance of the pectoral muscle on medio-lateral oblique (MLO) views increases the false positive rate in CADx. Therefore, the pectoral muscle should be identified and removed from the breast...
Breast cancer is the leading cause of death in women worldwide. Ultrasonography (USG) is one of the imaging modalities which is widely used to detect and classify the mass abnormalities of the breast nodule. The use of image processing in the development a computer aided diagnosis (CADx) can assist the radiologists in analysing and interpreting the abnormalities of ultrasound nodules. This paper proposes...
Breast mass segmentation in digital mammography is one of the most significant methods of breast cancer prevention. An integrated approach for mammographic mass segmentation is proposed in this paper. Given a mammographic image, it is first eliminated interference and enhanced in the preprocessing states. Then, the preprocessed images are detected and segmented by level set method. A preliminary evaluation...
Following paper presents new approach to the problem of automotive Ki-67 proliferation factor estimation in breast cancer biopsy images. This method is based on context filtering with designated neighborhood masks for edge detection. Proposed method was designed to compensate two major problems: cell shape divergence from ellipse and variability of color hue along with intensity in cell staining....
The article presents an innovative approach to the automatic detection of cells nuclei in FISH microscopic images. The proposed solution is based on L2 distance function which is used in patterns recognition in images. The results of researches show that an efficient identification of cells nuclei in FISH images with the presented method is possible. The numerical results show that the accuracy of...
Breast cancer is the most common category of cancers in woman around the world. Ultrasonography imaging modalities (US) are highly recommended for breast cancer examining due to their sensitivity, specificity, cost-effective, accessibility, portability, comfort, as well as non-invasive tool. In addition, an integration of a conventional US and its adjunct modalities which is Power Doppler has been...
Segmentation of masses in mammography images is an important task in early detection of breast cancer. Although the quality of segmentation is crucial to avoid misdiagnosis, the segmentation process is a challenging task even for specialists, due to the presence of ill-defined edges and low contrast images. In this work, we propose an improvement on Random Walker algorithm to segment masses, by applying...
Fluorescence in situ hybridization (FISH) is a technique that prepares acceptable results for molecular imaging biomarkers to precisely and dependably detect and diagnose disorders which are sign of cancers. Since contemporary manual FISH signal analysis is low-effective and inconsistent, it is an attractive research area to develop automated FISH image scanning systems and computer-aided diagnosis...
Segmentation of cellular structures with high accuracy has a crucial importance for the detection of cancerous regions in histopathologic images. The proper segmentation of cellular structures is one of the most important issues to be considered when making a diagnosis by pathologists. In this study, the contribution of the superpixel method to the segmentation of high-resolution histopathologic images...
Breast Cancer is highly predominant in women in today's world. It can start in the breast and can spread to other areas of the body in the course of time. Breast cancer is the second largest disease leading to the death of women. The disease is curable if detected early enough. A lot of research is being done to detect the cancer at the earliest. Early detection at the microcalcification stage can...
Neural Network is utilized as a developing analytic tool for the diagnosis of breast cancer. The goal of this research is to determine breast tumor from digital mammograms with a machine learning technique in view of RF and combination of RF-ELM classifier. For digital mammogram images, MIAS database is used. Preprocessing is usually needed to enhance the low quality of the image. The region of interest...
Exploring the spatial interactions between tumor and the inflammatory microenvironment using digital pathology image analysis can contribute to a better understanding of the immune function and tumor heterogeneity. We address this by providing tools able to reveal various metrics describing spatial relationships in the cancer ecosystem. The approach comprises nuclei segmentation and classification,...
Analysis and interpretation of stained tumor sections is one of the main tools in cancer diagnosis and prognosis, which is mainly carried out manually by pathologists. The avent of digital pathology provides us with the challenging opportunity to automatically analyze large amounts of these complex image data in order to draw biological conclusions from them and to study cellular and tissular phenotypes...
We introduce a new fully automated breast mass segmentation method from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The method is based on globally optimal inference in a continuous space (GOCS) using a shape prior computed from a semantic segmentation produced by a deep learning (DL) model. We propose this approach because the limited amount of annotated training samples does...
The main aim of this paper is to enhance using hybrid solution for early diagnosis of breast cancer using mammogram images. For the CAD system for any image processing system follows mainly four steps that is image pre processing, segmentation, features extraction and classification and evaluation. For this research purpose follows the same structure using best possible methods in each stage found...
Breast cancer is a significantly alarming health issue for women where Dynamic Contrast Enhanced Magnetic Resonance Imaging serves as a pivot in detection, diagnoses and treatment monitoring. In this paper the response given by breast cancer patients to Neoadjuvant Chemotherapy is analyzed with Magnetic Resonance Images of these patients taken before and after treatment. The MRI images are pre-processed...
Breast cancer is the leading cancer that affects women in the world. Early diagnosis of cancer prevents morbidity and mortality rate. Thermography is an additional tool for early diagnosis of breast cancer. Low contrast, poor Signal to Noise Ratio (SNR) and complex breast boundaries are the inherent limitation of breast thermal images which makes segmentation a challenging task. In this work, an attempt...
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