SEGMENTATION OF PROSTATE IN MRI IMAGES USING GRAPH-CUT SEGMENTATION

Prostate cancer has become one of the highest cancer-related death cases over the last few years in the West. This cancer affects only men. Statistics has shown that there is a big rise in the number of estimation cases over the last years. The increase in the number of these cases leads to accurate diagnoses at early stages enabling early intervention. Numbers of clinical practices are also introduced. One of these practices is the use of the Magnetic Resonance Imaging (MRI) scanner. However, images produced show a poor contrast of soft tissue between the surrounding tissue and prostate gland. This article aims to use the Graph-cut as the method segmentation of images. Index Terms – Prostate Gland, MRI scanner, MATLA

Recoloração de imagens para dicromatas baseada em mapas elásticos

TCC(graduação) - Universidade Federal de Santa Catarina. Campus Araranguá. Engenharia da Computação.A deficiência na percepção de cores (DPC) afeta 8% da população caucasiana masculina, causada pela falha ou ausência de células fotorreceptoras do tipo cone na retina, e proveniente de causa genética, alguma lesão no olho, ou também devido a outras doenças, como diabetes, leucemia, etc. O indivíduo com DPC tem dificuldades na percepção de cores, que variam dependendo do tipo de deficiência. Dicromatas são os indivíduos com DPC causada pela ausência de um dos tipos de fotorreceptores cone, causando dificuldades na percepção das cores. A DPC causa dificuldades na realização de tarefas que necessitam da distinção de cores, o que pode prejudicar o indivíduo tanto na vida pessoal quanto profissional. Este trabalho propõe uma técnica de recoloração de imagens para dicromatas baseada na técnica de redução de dimensionalidade Mapas Elásticos, onde o objetivo é proporcionar aos indivíduos imagens que preservam detalhes da imagem original, como contrastes entre cores, os quais, os dicromatas não percebem. A técnica foi implementada tanto para CPU como para GPU, apresentando bons tempos de execução, além de apresentar bons resultados no aspecto da preservação de contrastes após a recoloração, a técnica também se propõe a preservar o aspecto de naturalidade da imagem, escolhendo o mapeamento final que minimiza a soma total das distância entre a cor original e o mapeamento dela no plano de percepção dos dicromatas.Color Vision Deficiency (CVD) affects 8% of caucasian male populations, caused by failure or absence of cone-like photorreceptor cells in the retina. CVD may be from genetic cause, some eye injury, or from other diseases such as diabetes, leukemia, etc. Individuals with CVD have difficulty in color perception, whose variation depends on the type of disability. Dichromats are individuals with CVD caused by the abscence of one of the types of cone photoreceptors, causing difficulties in the perception of colors. CVD causes difficulties in performing tasks that require color distinction, which can harm the individual in both personal and professional life. This work proposes an image recoloring technique for dichromats based on the Elastic Maps dimensionality reduction technique, where the objective is to provide images that preserve details of the original image, such as color contrasts. The technique was implemented both CPU and GPU, presenting good execution times, and good results in the aspect of preservation of contrasts after recoloring, the technique also proposes to preserve the aspect of naturality of image, choosing the final mapping that minimizes the total sum of the distance between the original color and the mapping of it in the plane of dichromat perception

A Region-based Randers Geodesic Approach for Image Segmentation

The minimal path model based on the Eikonal partial differential equation has served as a fundamental tool for the applications of image segmentation and boundary detection in the passed two decades. However, the existing approaches commonly only exploit the image edge-based features for computing minimal paths, potentially limiting their performance in complicated segmentation situations. In this paper, we introduce a new variational image segmentation model based on the minimal path framework and the eikonal PDE, where the region-based appearance term that defines then regional homogeneity features can be taken into account for estimating the associated minimal paths. This is done by constructing a Randers geodesic metric interpretation to the region-based active contour energy. As a result, the minimization of the active contour energy is transformed to finding the solution to the Randers eikonal PDE. We also suggest a practical interactive image segmentation strategy, where the target boundary can be delineated by the concatenation of the piecewise geodesic paths. We invoke the Finsler variant of the fast marching method to estimate the geodesic distance map, yielding an efficient implementation of the proposed Eikonal region-based active contour model. Experimental results on both synthetic and real images exhibit that our model indeed achieves encouraging segmentation performance

Development of Anatomical and Functional Magnetic Resonance Imaging Measures of Alzheimer Disease

Alzheimer disease is considered to be a progressive neurodegenerative condition, clinically characterized by cognitive dysfunction and memory impairments. Incorporating imaging biomarkers in the early diagnosis and monitoring of disease progression is increasingly important in the evaluation of novel treatments. The purpose of the work in this thesis was to develop and evaluate novel structural and functional biomarkers of disease to improve Alzheimer disease diagnosis and treatment monitoring. Our overarching hypothesis is that magnetic resonance imaging methods that sensitively measure brain structure and functional impairment have the potential to identify people with Alzheimer’s disease prior to the onset of cognitive decline. Since the hippocampus is considered to be one of the first brain structures affected by Alzheimer disease, in our first study a reliable and fully automated approach was developed to quantify medial temporal lobe atrophy using magnetic resonance imaging. This measurement of medial temporal lobe atrophy showed differences (pnovel biomarker of brain activity was developed based on a first-order textural feature of the resting state functional magnetic resonance imagining signal. The mean brain activity metric was shown to be significantly lower (pp18F labeled fluorodeoxyglucose positron emission tomography. In the final study, we examine whether combined measures of gait and cognition could predict medial temporal lobe atrophy over 18 months in a small cohort of people (N=22) with mild cognitive impairment. The results showed that measures of gait impairment can help to predict medial temporal lobe atrophy in people with mild cognitive impairment. The work in this thesis contributes to the growing evidence the specific magnetic resonance imaging measures of brain structure and function can be used to identify and monitor the progression of Alzheimer’s disease. Continued refinement of these methods, and larger longitudinal studies will be needed to establish whether the specific metrics of brain dysfunction developed in this thesis can be of clinical benefit and aid in drug development