Aristófanes Corrêa Silva

Classification of breast tissues using Moran's index and Geary's coefficient as texture signatures and SVM

Female breast cancer is the major cause of cancer-related deaths in western countries. Efforts in computer vision have been made in order to help improving the diagnostic accuracy by radiologists. In this paper, we present a methodology that uses Morans index and Gearys coefficient measures in breast tissues extracted from mammogram images. These measures are used as input features for a support vector machine classifier with the purpose of distinguishing tissues between normal and abnormal cases as well as classifying them into benign and malignant cancerous cases. The use of both proposed techniques showed to be very promising, since we obtained an accuracy of 96.04% and Az ROC of 0.946 with Gearys coefficient and an accuracy of 99.39% and Az ROC of 1 with Morans index to discriminate tissues in mammograms as normal or abnormal. We also obtained accuracy of 88.31% and Az ROC of 0.804 with Gearys coefficient and accuracy of 87.80% and Az ROC of 0.89 with Morans index to discriminate tissues in mammograms as benign and malignant.

Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index

OBJECTIVE The present work has the objective of developing an automatic methodology for the detection of lung nodules. METHODOLOGY The proposed methodology is based on image processing and pattern recognition techniques and can be summarized in three stages. In the first stage, the extraction and reconstruction of the pulmonary parenchyma is carried out and then enhanced to highlight its structures. In the second stage, nodule candidates are segmented. Finally, in the third stage, shape and texture features are extracted, selected and then classified using a support vector machine. RESULTS In the testing stage, with 140 new exams from the Lung Image Database Consortium image collection, 80% of which are for training and 20% are for testing, good results were achieved, as indicated by a sensitivity of 85.91%, a specificity of 97.70% and an accuracy of 97.55%, with a false positive rate of 1.82 per exam and 0.008 per slice and an area under the free response operating characteristic of 0.8062. CONCLUSION Lung cancer presents the highest mortality rate in addition to one of the smallest survival rates after diagnosis. An early diagnosis considerably increases the survival chance of patients. The methodology proposed herein contributes to this diagnosis by being a useful tool for specialists who are attempting to detect nodules.

Automatic segmentation of lung nodules with growing neural gas and support vector machine

Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancer. Unfortunately, this disease is often diagnosed late, affecting the treatment outcome. In order to help specialists in the search and identification of lung nodules in tomographic images, many research centers have developed computer-aided detection systems (CAD systems) to automate procedures. This work seeks to develop a methodology for automatic detection of lung nodules. The proposed method consists of the acquisition of computerized tomography images of the lung, the reduction of the volume of interest through techniques for the extraction of the thorax, extraction of the lung, and reconstruction of the original shape of the parenchyma. After that, growing neural gas (GNG) is applied to constrain even more the structures that are denser than the pulmonary parenchyma (nodules, blood vessels, bronchi, etc.). The next stage is the separation of the structures resembling lung nodules from other structures, such as vessels and bronchi. Finally, the structures are classified as either nodule or non-nodule, through shape and texture measurements together with support vector machine. The methodology ensures that nodules of reasonable size be found with 86% sensitivity and 91% specificity. This results in a mean accuracy of 91% for 10 experiments of training and testing in a sample of 48 nodules occurring in 29 exams. The rate of false positives per exam was of 0.138, for the 29 exams analyzed.

Classification of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and SVM

Breast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts identify suspicious areas that are difficult to perceive with the human eye, thus aiding in the detection and diagnosis of cancer. This work proposes a methodology for the discrimination and classification of regions extracted from mammograms as mass and non-mass. The Digital Database for Screening Mammography (DDSM) was used in this work for the acquisition of mammograms. The taxonomic diversity index (Δ) and the taxonomic distinctness (Δ(⁎)), which were originally used in ecology, were used to describe the texture of the regions of interest. These indexes were computed based on phylogenetic trees, which were applied to describe the patterns in regions of breast images. Two approaches were used for the analysis of texture: internal and external masks. A support vector machine was used to classify the regions as mass and non-mass. The proposed methodology successfully classified the masses and non-masses, with an average accuracy of 98.88%.

Detection of Breast Masses in Mammogram Images Using Growing Neural Gas Algorithm and Ripley's K Function

Breast cancer is a serious public health problem in several countries. Computer-aided detection/diagnosis systems (CAD/CADx) have been used with relative success in aid of health care professionals. The goal of such systems is not to replace the professionals, but to join forces in order to detect the different types of cancer at an early stage. The main contribution of this work is the presentation of a methodology for detecting masses in digitized mammograms using the growing neural gas algorithm for image segmentation and Ripley’s K function to describe the texture of segmented structures. The classification of these structures is accomplished through support vector machines which separate them in two groups, using shape and texture measures: masses and non-masses. The methodology obtained 89.30% of accuracy and a rate of 0.93 false positives per image.

Diagnosis of breast cancer in digital mammograms using independent component analysis and neural networks

We propose a method for discrimination and classification of mammograms with benign, malignant and normal tissues using independent component analysis and neural networks. The method was tested for a mammogram set from MIAS database, and multilayer perceptron neural networks, probabilistic neural networks and radial basis function neural networks. The best performance was obtained with probabilistic neural networks, resulting in 97.3% success rate, with 100% of specificity and 96% of sensitivity.

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.

Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM

Lung cancer stands out among all other types of cancer for presenting one of the highest incidence rates and one of the highest rates of mortality. Unfortunately, this disease is often diagnosed late, affecting the treatment result. One of the hopes for changing this scenario lies in achieving a more precocious diagnosis of lung cancer through low-dose computed tomography, used as a screening method in risk groups of smokers or former smokers with elevated tobacco load. In order to help specialists in this search and identification of lung nodules in tomographic images, many research centers develop computer-aided detection systems (CAD systems) which are intended to automate procedures. This work has the purpose of developing a methodology for automatic detection of small lung nodules (with sizes between 2 and 10mm) through image processing and pattern recognition techniques. Some of these techniques are widely used in similar applications, as is the case of the region growing technique for segmentation of the pulmonary parenchyma. Other techniques, with more restricted application, are the Gaussian mixture models and the Hessian matrix for segmentation of structures inside the lung, Tsalliss and Shannons entropy measurements as texture descriptors, and support vector machine to classify suspect regions as either nodules or non-nodules. The results achieved with the use of this set of techniques, applied to a sample with 28 exams from a public database, showed that small nodules were detected with a sensitivity of 90.6%, a specificity of 85% and an accuracy of 88.4%. The rate of false positives per exam was of 1.17. Graphical abstractDisplay Omitted HighlightsWe present a methodology for automatic detection of small lung nodules.Gaussian Mixture Models are used to segment regions that are likely to be nodules.False positive reduction with SVM and entropy measures of Tsallis and Shannon.Tests use a sample of 72 nodules occurring in 28 exams from LIDC image database.Presents sensitivity of 90.6%, specificity of 85%, accuracy of 88.4%, and 1.17 FP/i.

Detection of masses in mammographic images using geometry, Simpson's Diversity Index and SVM

This paper presents a computational methodology to detect masses in mammographic images. In the first step, the K-means clustering algorithm and the template-matching technique are used to detect suspicious regions. Next, geometry and texture features of each region are extracted. Texture is described using Simpsons Diversity Index, which is used in Ecology to measure the biodiversity of an ecosystem. Finally, the information of texture is used by Support Vector Machine (SVM) to classify the suspicious regions into two classes: masses and non-masses. The tests demonstrate that the methodology has 83.94% of accuracy, 83.24% of sensitivity and 84.14% of specificity.

Diagnosis of lung nodule using Moran’s index and Geary’s coefficient in computerized tomography images

This paper analyzes the application of Moran’s index and Geary’s coefficient to the characterization of lung nodules as malignant or benign in computerized tomography images. The characterization method is based on a process that verifies which combination of measures, from the proposed measures, has been best able to discriminate between the benign and malignant nodules using stepwise discriminant analysis. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these in predicting the classification for each nodule. In order to verify this application we also describe tests that were carried out using a sample of 36 nodules: 29 benign and 7 malignant. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. The two analyzed functions and its combinations have provided above 90% of accuracy and a value area under receiver operation characteristic (ROC) curve above 0.85, that indicates a promising potential to be used as nodules signature measures. The preliminary results of this approach are very encouraging in characterizing nodules using the two functions presented.