Simara Vieira da Rocha

A mass classification using spatial diversity approaches in mammography images for false positive reduction

Abstract Breast cancer is configured as a public health problem that affects mainly women population. One of the main ways of prevention is through screening mammography. The interpretation made by the physician is a repetitive task because of a low contrast image and the examination of several exams. So, computer systems have been proposed to aid detection step and helps physician, with the aim to increase sensitivity at the same time that reduces invasive procedures. Although these systems had improved the sensitivity of the original examination of mammography, they also generate a lot of false positives. This paper presents a methodology for reducing false positives by analyzing the diversity of approaches with improved spatial decomposition. After experiments the results reaches a high level of sensitivity at the same time promote a high rate of reduction of false positives.

Texture analysis of masses malignant in mammograms images using a combined approach of diversity index and local binary patterns distribution

A methodology to texture analysis of masses in digitized mammography is proposed.Our methodology uses only the texture analysis for recognition.We provide the specialist bigger support to the diagnosis of breast cancer.We cooperate to a more precise diagnosis and support in the medical intervention. A World Health Organization (WHO) report estimates that in 2015, at least 561 thousand women will die of breast cancer. Although breast cancer is considered a disease of the developed world, nearly 50% of the cases and 58% of the deaths occur in the less developed countries. A mammogram is a way of discovering not just the palpable tumors that cause cancer but also other lesions that are not perceived during the physical examination performed by the expert physician or during self-exams; however, it is known that this exam is targeted for women after the age of 40 because age is one of the factors that can cause great variations in sensitivity during the exam. Besides the patients age, the experts experience and the quality of the images obtained during the exam are decisive factors in the detection of breast cancer. This work presents two novelties. The first is the use of Local Binary Patterns (LBPs) to generate a representation of a Region of Interest (ROI) image. Over this representation, we generate other representations using techniques such as image histograms, gray-level co-occurrence matrices (GLCMs) and gray-level run-length matrices (GLRLMs). These representations allow texture analysis through several perspectives. The second novelty uses these representations as input to the application of indexes adapted from ecology (Shannon, McIntosh, Simpson, Gleason and Menhinick) as texture descriptors. Based on this strategy, we analyze mammographic image textures to classify regions of these images as benign or malignant using a Support Vector Machine (SVM). The best result achieved was of 88.31% accuracy, 85% sensitivity, 91.89% specificity, a positive probability ratio of 10.48, a negative probability ratio of 0.16, and an area under the Receiver Operating Characteristic (ROC) curve of 0.88, obtained through the Shannon index. We believe that the proposed method, with some adaptations, may also be used for image texture analysis of several different lesions such as lung nodules, glaucoma and prostates. This belief is based on the achieved results and the methods simplicity.

Diagnosis of breast tissue in mammography images based local feature descriptors

Breast cancer is one of the leading causes of death by cancer among women. The high mortality rates and the occurrence of this cancer worldwide show the importance of the investigation and development of means for the detection and early diagnosis of this disease. Computer-Aided Detection and Diagnosis systems have been developed to improve diagnostic accuracy by radiologists. This work proposes a method for discriminating patterns of malignancy and benignity of masses in digitized mammography images through the analysis of local features. The method comparatively applies the Scale-Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF), Oriented Fast and Rotated BRIEF (ORB) and Local Binary Pattern (LBP) descriptors for local feature extraction. These features are represented by a Bag of Features (BoF) model, applied to provide new representations of the data and to reduce its dimensionality. Finally, the features are used as input for the Support Vector Machine (SVM), Adaptive Boosting (Adaboost) and Random Forests (RF) classifiers to differentiate malignant and benign masses. The method obtained significant results, reaching 100% sensitivity, 99.65% accuracy and 99.24% specificity for benign and malignant mass classification.

Breast cancer detection in mammography using spatial diversity, geostatistics, and concave geometry

Breast cancer is a global health problem which mainly affects the female population. It is known that early detection increases the chances of effective treatment, improving the disease prognosis. It remains a challenge to detect the lesion with high detection rate and ensure, at the same time, low rates of false positives . Aiming this objective, this work proposes an efficient method for detection of mass regions on digitized mammograms though diversity analysis, geostatistical and concave geometry (Alpha Shapes). We evaluate the detection rate for each feature extraction using Support Vector Machine in MIAS and DDSM database, with 74 and 621 mammograms, respectively, all containing at least one mass region. The obtained results are promising, reaching 97.30% of detection rate and 0.89 false positive per image for MIAS database and also 91.63% of detection rate and 0.86 false positive per image for DDSM database. Specifically, in DDSM obtaining high detection rate and low rate of false positives when using concave geometry to extract features in a large database.

DIAGNOSIS OF BREAST REGIONS THROUGH THE USE OF RIPLEY'S K FUNCTION AND SVM

Breast cancer has become increasingly common among the female population over 40 years and is the type of cancer that affects more women worldwide. One way to early detect non-palpable tumors that cause breast cancer is to perform an X-ray (mammogram) of the breasts. It is known that the chances of curing breast cancer is high if detected in early stages. However, the sensitivity of this test may vary widely due to factors such as examination quality or specialist experience. Thus, the use of diagnose systems in order to assist the specialist, have increased the chances of correct diagnoses. This paper presents a methodology for image spatial texture analysis and recognition of patterns present in mass extracted from images of mammograms, according to their malignant or benign behavior. Therefore, this paper uses the Ripley’s K function to extract texture and SVM for pattern recognition. The best result achieved was 85% accuracy, 88.23% sensitivity and 80.76% specificity with Az = 0.84.