Wellington Pinheiro dos Santos

Detection and classification of masses in mammographic images in a multi-kernel approach

BACKGROUND AND OBJECTIVE According to the World Health Organization, breast cancer is the main cause of cancer death among adult women in the world. Although breast cancer occurs indiscriminately in countries with several degrees of social and economic development, among developing and underdevelopment countries mortality rates are still high due to low availability of early detection technologies. From the clinical point of view, mammography is still the most effective diagnostic technology, given the wide diffusion of the use and interpretation of these images. METHODS Herein this work we propose a method to detect and classify mammographic lesions using the regions of interest of images. Our proposal consists in decomposing each image using multi-resolution wavelets. Zernike moments are extracted from each wavelet component. Using this approach, we can combine both texture and shape features, which can be applied both to the detection and classification of mammary lesions. We used 355 images of fatty breast tissue of IRMA database, with 233 normal instances (no lesion), 72 benign, and 83 malignant cases. RESULTS Classification was performed by using SVM and ELM networks with modified kernels in order to optimize accuracy rates, reaching 94.11%. Considering both accuracy rates and training times, we defined the ration between average percentage accuracy and average training time in a reverse order. Our proposal was 50 times higher than the ratio obtained using state-of-the-art approaches. CONCLUSIONS As our proposed model can combine high accuracy rate with low learning time, whenever a new data is received, our work will be able to save a lot of time, hours, in learning process in relation to the best method of the state-of-the-art.We propose a method to detect and classify mammographic lesions using the regions of interest of images.We use multi-resolution wavelets and Zernike moments as extract feature extractor image stage.We can combine both texture and shape features, which can be applied both to the detection and classification of mammary lesions.Considering the ratio between accuracy and training time, our proposal proved to be 50 times superior to state-of-the-art approaches.As our proposed model can combine high accuracy rate with low learning time, whenever a new data is received, our work will be able to save a lot of time, hours, in learning process in relation to the best method of the state-of-the-art approaches. Background and ObjectiveAccording to the World Health Organization, breast cancer is the main cause of cancer death among adult women in the world. Although breast cancer occurs indiscriminately in countries with several degrees of social and economic development, among developing and underdevelopment countries mortality rates are still high due to low availability of early detection technologies. From the clinical point of view, mammography is still the most effective diagnostic technology, given the wide diffusion of the use and interpretation of these images. MethodsHerein this work we propose a method to detect and classify mammographic lesions using the regions of interest of images. Our proposal consists in decomposing each image using multi-resolution wavelets. Zernike moments are extracted from each wavelet component. Using this approach, we can combine both texture and shape features, which can be applied both to the detection and classification of mammary lesions. We used 355 images of fatty breast tissue of IRMA database, with 233 normal instances (no lesion), 72 benign, and 83 malignant cases. ResultsClassification was performed by using SVM and ELM networks with modified kernels in order to optimize accuracy rates, reaching 94.11%. Considering both accuracy rates and training times, we defined the ration between average percentage accuracy and average training time in a reverse order. Our proposal was 50 times higher than the ratio obtained using state-of-the-art approaches. ConclusionsAs our proposed model can combine high accuracy rate with low learning time, whenever a new data is received, our work will be able to save a lot of time, hours, in learning process in relation to the best method of the state-of-the-art.

An adaptive fuzzy-based system to simulate, quantify and compensate color blindness

About 8% of the male population of the world are affected by a determined type of color vision disturbance, which varies from the partial to complete reduction of the ability to distinguish certain colors. A considerable amount of color blind people are able to live all life long without knowing they have color vision disabilities and abnormalities. Nowadays the evolution of information technology and computer science, specifically image processing techniques and computer graphics, can be fundamental to aid at the development of adaptive color blindness correction tools. This paper presents a software tool based on Fuzzy Logic to evaluate the type and the degree of color blindness a person suffer from. In order to model several degrees of color blindness, herein this work we modified the classical linear transform-based simulation method by the use of fuzzy parameters. We also proposed four new methods to correct color blindness based on a fuzzy approach: Methods A and B, with and without histogram equalization. All the methods are based on combinations of linear transforms and histogram operations. In order to evaluate the results we implemented a web-based survey to get the best results according to optimize to distinguish different elements in an image. Results obtained from 40 volunteers proved that the Method B with histogram equalization got the best results for about 47% of volunteers.

Image segmentation of ovitraps for automatic counting of Aedes Aegypti eggs

The Aedes Aegypti mosquito is the vector of the most difficult public health problems in tropical and semi-tropical world: the epidemic proliferation of dengue, a viral disease that can cause human beings death specially in its most dangerous form, dengue haemorrhagic fever. One of the most useful methods for mosquito detection and surveillance is the ovitraps: special traps to collect eggs of the mosquito. It is very important to count the number of Aedes Aegypti eggs present in ovitraps. This counting is usually performed in a manual, visual and non-automatic form. This work approaches the development of automatic methods to count the number of eggs in ovitraps images using image processing, particularly color segmentation and mathematical morphology-based non-linear filters.

Evaluation of Alzheimer's disease by analysis of MR images using multilayer perceptrons and committee machines.

Alzheimers disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and its correlation with the advance of Alzheimers disease. The MR images were acquired from a unique volunteer with Alzheimers, using an image system based on a clinical 1.5T tomographer. The classification methods are based on multilayer perceptrons and committee machines and the classification results are used to correlate clinical and imaging findings. The classification results are used to improve the usual analysis of the ADC map.

An adaptive semi-supervised Fuzzy GrowCut algorithm to segment masses of regions of interest of mammographic images

Graphical abstractDisplay Omitted According to the World Health Organization, breast cancer is the most common cancer in women worldwide, becoming one of the most fatal types of cancer. Mammography image analysis is still the most effective imaging technology for breast cancer diagnosis, which is based on texture and shape analysis of mammary lesions. The GrowCut algorithm is a general-purpose segmentation method based on cellular automata, able to perform relatively accurate segmentation through the adequate selection of internal and external seed points. In this work we propose an adaptive semi-supervised version of the GrowCut algorithm, based on the modification of the automaton evolution rule by adding a Gaussian fuzzy membership function in order to model non-defined borders. In our proposal, manual selection of seed points of the suspicious lesion is changed by a semiautomatic stage, where just the internal points are selected by using a differential evolution algorithm. We evaluated our proposal using 57 lesion images obtained from MiniMIAS database. Results were compared with the semi-supervised state-of-the-art approaches BEMD, BMCS, Wavelet Analysis, LBI, Topographic Approach and MCW. Results show that our method achieves better results for circumscribed, spiculated lesions and ill-defined lesions, considering the similarity between segmentation results and ground-truth images.

A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic images

We propose a Fuzzy semi-supervised version of the GrowCut algorithm.We reduced dependence of GrowCut on user experience, using simulated annealing.To improve robustness to point selection, we modified the GrowCut evolution rule.We evaluated our approach by classifying 685 digital mammograms.Our approach could reach an overall accuracy of 91% for fat tissues. According to the World Health Organization, breast cancer is the most common form of cancer in women. It is the second leading cause of death among women round the world, becoming the most fatal form of cancer. Despite the existence of several imaging techniques useful to aid at the diagnosis of breast cancer, x-ray mammography is still the most used and effective imaging technology. Consequently, mammographic image segmentation is a fundamental task to support image analysis and diagnosis, taking into account shape analysis of mammary lesions and their borders. However, mammogram segmentation is a very hard process, once it is highly dependent on the types of mammary tissues. The GrowCut algorithm is a relatively new method to perform general image segmentation based on the selection of just a few points inside and outside the region of interest, reaching good results at difficult segmentation cases when these points are correctly selected. In this work we present a new semi-supervised segmentation algorithm based on the modification of the GrowCut algorithm to perform automatic mammographic image segmentation once a region of interest is selected by a specialist. In our proposal, we used fuzzy Gaussian membership functions to modify the evolution rule of the original GrowCut algorithm, in order to estimate the uncertainty of a pixel being object or background. The main impact of the proposed method is the significant reduction of expert effort in the initialization of seed points of GrowCut to perform accurate segmentation, once it removes the need of selection of background seeds. Furthermore, the proposed method is robust to wrong seed positioning and can be extended to other seed based techniques. These characteristics have impact on expert and intelligent systems, once it helps to develop a segmentation method with lower required specialist knowledge, being robust and as efficient as state of the art techniques. We also constructed an automatic point selection process based on the simulated annealing optimization method, avoiding the need of human intervention. The proposed approach was qualitatively compared with other state-of-the-art segmentation techniques, considering the shape of segmented regions. In order to validate our proposal, we built an image classifier using a classical multilayer perceptron. We used Zernike moments to extract segmented image features. This analysis employed 685 mammograms from IRMA breast cancer database, using fat and fibroid tissues. Results show that the proposed technique could achieve a classification rate of 91.28% for fat tissues, evidencing the feasibility of our approach.

Avaliação Da Doença De Alzheimer Pela Análise Multiespectral De Imagens DW-MR Por Redes BRF Como Alternativa Aos Mapas ADC

Alzheimers disease is the most common cause of dementia, yet difficult to accurately diagnose without the use of invasive techniques, particularly at the beginning of the disease. This work addresses the classification and analysis of multispectral synthetic images composed by diffusion-weighted magnetic resonance brain volumes for evaluation of the area of cerebrospinal fluid and its correlation with the progression of Alzheimers disease. A 1.5 T MR imaging system was used to acquire all the images presented. The classification methods are based on multilayer perceptrons and classifiers of radial basis function networks. It is assumed that the classes of interest can be separated by hyperquadrics. A polynomial network of degree 2 is used to classify the original volumes, generating a ground-truth volume. The classification results are used to improve the usual analysis by the map of apparent diffusion coefficients.

Reconstruction of electrical impedance tomography images using genetic algorithms and non-blind search

The development and improvement of non-invasive imaging techniques have been increasing in the last decades, due to interests from both academy and industry. Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that offers a vast field of possibilities due to its low cost, portability, and safety of handling. However, EIT image reconstruction is an ill-posed problem. Herein this work we present an EIT reconstruction method based on the optimization of the relative error of reconstruction using genetic algorithms employing elitist strategies. The initial set of solutions used by the elitist genetic algorithm includes a noisy version of the solution obtained from the backprojection algorithm, according to Saha and Bandyopadhyays criterion for non-blind initial search in optimization algorithms, in order to accelerate convergence and improve performance.

Segmentation of mammography by applying extreme learning machine in tumor detection

Locating regions of tumor in digital mammography images is a hard task even for experts. Consequently, due to medical experience, different diagnoses to an image are commonly found. Therefore, the use of an automatic approach for detecting tumor regions is important to avoid misdiagnosis. In this work, the Extreme Learning Machine (ELM) neural network was used to segment tumor regions of digitized mammograms available in the Mini-Mias database. A set of images were selected for training, while different images were used for testing. Results showed that ELM provides an over 81% classification rate, being able to segment the region of tumor with high accuracy. By comparing ELM with MLP network, it was possible to conclude that ELM has a faster learning time, with a higher training and testing accuracy.

Mammographic images segmentation using texture descriptors

Tissue classification in mammography can help the diagnosis of breast cancer by separating healthy tissue from lesions. We present herein the use of three texture descriptors for breast tissue segmentation purposes: the Sum Histogram, the Gray Level Co-Occurrence Matrix (GLCM) and the Local Binary Pattern (LBP). A modification of the LBP is also proposed for a better distinction of the tissues. In order to segment the image into its tissues, these descriptors are compared using a fidelity index and two clustering algorithms: k-Means and SOM (Self-Organizing Maps).