Microcalcifications Detection Using Image And Signal Processing Techniques For Early Detection Of Breast Cancer

Breast cancer has transformed into a severe health problem around the world. Early diagnosis is an important factor to survive this disease. The earliest detection signs of potential breast cancer that is distinguishable by current screening techniques are the presence of microcalcifications (MCs). MCs are small crystals of calcium apatite and their normal size ranges from 0.1mm to 0.5mm single crystals to groups up to a few centimeters in diameter. They are the first indication of breast cancer in more than 40% of all breast cancer cases, making their diagnosis critical. This dissertation proposes several segmentation techniques for detecting and isolating point microcalcifications: Otsu’s Method, Balanced Histogram Thresholding, Iterative Method, Maximum Entropy, Moment Preserving, and Genetic Algorithm. These methods were applied to medical images to detect microcalcifications. In this dissertation, results from the application of these techniques are presented and their efficiency for early detection of breast cancer is explained. This dissertation also explains theories and algorithms related to these techniques that can be used for breast cancer detection

AN AUTOMATED COMPUTER-AIDED DETECTION (CADe) AND DIAGNOSIS (CADx) SYSTEM FOR BREAST MICROCALCIFICATIONS IN MAMMOGRAMS

ABSTRACTAn automated computer aided diagnosis system has been proposed for detection of microcalcification (MC) clusters in mammograms. The proposed system is a whole system including suspicious regions identification, MCs detection, false positive reduction and benign/malign classification. For classification of suspicious microcalcification regions, a multilayer perceptron (MLP) neural network was used with grey level co-occurrence matrix (GLCM) and statistical features.  Then to decrease the false positive classification ratio, we used cascade correlation neural network (CCNN) with grey level run length matrix (GLRLM) features. In the last step, hybrid form of discriminant analysis and support vector machine (SVM) methods were used with GLRLM features for benign/malign classification of detected MC clusters. The open access Mammographic Image Analysis Society (MIAS) database was used for the study. Experimental results show that the proposed algorithm obtained 86% sensitivity, 98.3% specificity and 1.163 FPpI rates for detection an for diagnosis of breast cancer, the obtained sensitivity and specificity values are 100% and 100% respectively. Despite the vision difficulty of MC clusters, the novel system provides very satisfactory results. Furthermore, the developed system is fully automatic whole system which gives outputs as percentages and transformed assessment categories. Keywords: Mammograms, Breast cancer, Computer aided diagnosis, Cascade correlation neural network (CCNN), Grey level co-occurrence matrix (GLCM), Grey level run length matrix (GLRLM). 

Performance of a CAD scheme applied to images obtained from mammographic film digitization and full-field digital mammography (FFDM).

This work has as purpose to compare the effects of a CAD scheme applied to digitized and \ud direct digital mamograms sets. A routine designed to be applied to mammogram in \ud DICOM standard was developed and a schema based on the Watershed Transform to \ud masses detection was applied to 252 ROIs from 130 digitized mammograms, resulting in \ud 92% of true positive and 10% of false positives. For clustered microcalcifications \ud detection, another procedure was applied to 165 ROIs from 120 mammograms, resulting in \ud 93% of true positive and 16% of false positive. By using the same procedures to 154 \ud digital mammograms obtained from FFDM, the rates have shown a little decrease in the \ud scheme performance: 89% of true positive and 16% of false positive for masses detection; \ud 90% of true positive and 27% of false positive for clusters detection. Although the tests \ud with digital mammograms have been carried with a smaller number of images and \ud different cases compared to the digitized ones, including several dense breasts images, the \ud results can be considered comparable, mainly forclustered microcalcifications detection \ud with a difference of only 3% between the sensibility rates for the both images sets. Another \ud important feature affecting these results is the contrast difference between the two images \ud set. This implies the need of extensive investigations not only with a larger number of \ud cases from FFDM but also on the parameters related to its image acquisition as well as to \ud its corresponding processing.Este trabalho tem como objetivo comparar os resultados de um esquema CAD aplicado em \ud conjunto de mamografias digitalizadas e em um conjunto de mamografias obtidas de um \ud mamógrafo digital. Para extrair as imagens do padrão DICOM, padrão utilizado pelos \ud mamógrafos digitais, uma rotina computacional foi desenvolvida. Para a detecção de \ud nódulos, um esquema baseado em Transforma Watershed foi aplicado a 252 regiões de \ud interesse (ROIs) de 130 mamografias digitalizadas, resultando em 92% de verdadeiro \ud positivo e 10%de falsos positivos. Para a detecção de microcalcificações agrupadas, outro \ud procedimento foi aplicado a165 ROIs extraídas de 120 mamografias digitalizadas, \ud resultando em 93% de verdadeiro positivo e 16% de falso positivo. Ao utilizar os mesmos \ud procedimentos para154 mamografias digitais obtidas a partir de um FFDM, as taxas \ud mostraram uma diminuição pequena no desempenho: 89% do verdadeiro positivo e 16% \ud de falso positivo para a detecção de nódulos, e 90% de verdadeiro positivo e 27% de falsos \ud positivo para a detecção de clusters de microcalcificações. Embora os testes com \ud mamografias digitais tenham sido realizados com um menor número de imagens e casos \ud diferentes em comparação com os digitalizados, incluindo várias imagens de mamas \ud densas, os resultados podem ser considerados comparáveis, principalmente para a detecção \ud de clusters de microcalcificações com uma diferença de apenas 3% entre as taxas de \ud sensibilidade para as imagens dos dois conjuntos. Outra característica importante que afeta \ud esses resultados é a diferença de contraste dos dois grupos de imagens analisados. Isto \ud implica na necessidade de extensas investigações não só com um maior número de casos \ud de mamografias digitais, mas também um estudo sobre os parâmetros relacionados a \ud aquisição da imagem, bem como para o seu processamentoCNPqFAPESPHospital of Clinics in Botucatu/S

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon's-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images. (C) 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences

A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM

In digital mammograms, an early sign of breast cancer is the existence of microcalcification clusters (MCs), which is very important to the early breast cancer detection. In this paper, a new approach is proposed to classify and detect MCs. We formulate this classification problem as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a “vocabulary” of visual parts. A visual information-rich vocabulary of training samples is manually built up from a set of samples, which include MCs parts and no-MCs parts. With the prior ground truth of MCs in mammograms, the sparse feature learning is acquired by the lP-regularized least square approach with the interior-point method. Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs). To investigate its performance, the proposed method is applied to DDSM datasets and compared with support vector machines (SVMs) with the same dataset. Experiments have shown that performance of the proposed method is more efficient or better than the state-of-art methods

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further