Deformable Model for 3D Intramodal Nonrigid Breast Image Registration with Fiducial Skin Markers

We implemented a new approach to intramodal non-rigid 3D breast image registration. Our method uses fiducial skin markers (FSM) placed on the breast surface. After determining the displacements of FSM, finite element method (FEM) is used to distribute the markers’ displacements linearly over the entire breast volume using the analogy between the orthogonal components of the displacement field and a steady state heat transfer (SSHT). It is valid because the displacement field in x, y and z direction and a SSHT problem can both be modeled using LaPlace’s equation and the displacements are analogous to temperature differences in SSHT. It can be solved via standard heat conduction FEM software with arbitrary conductivity of surface elements significantly higher than that of volume elements. After determining the displacements of the mesh nodes over the entire breast volume, moving breast volume is registered to target breast volume using an image warping algorithm. Very good quality of the registration was obtained. Following similarity measurements were estimated: Normalized Mutual Information (NMI), Normalized Correlation Coefficient (NCC) and Sum of Absolute Valued Differences (SAVD). We also compared our method with rigid registration technique

Automated Segmentation of Cells with IHC Membrane Staining

This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and quantification of the target membrane proteins that is fundamental in several medical applications such as cancer characterization and classification, personalized therapy design, and for any other applications requiring cell morphology characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide applicability and high accuracy of our approach in the context of IHC analysi

Development and characterization of methodology and technology for the alignment of fMRI time series

This dissertation has developed, implemented and tested a novel computer based system (AUTOALIGN) that incorporates an algorithm for the alignment of functional Magnetic Resonance Image (fMRI) time series. The algorithm assumes the human brain to be a rigid body and computes a head coordinate system on the basis of three reference points that lie on the directions correspondent to two of the eigenvectors of inertia of the volume, at the intersections with the head boundary. The eigenvectors are found weighting the inertia components with the voxel\u27s intensity values assumed as mass. The three reference points are found in the same position, relative to the origin of the head coordinate system, in both test and reference brain images. Intensity correction is performed at sub-voxel accuracy by tri-linear interpolation. A test fMR brain volume in which controlled simulations of rigid-body transformations have been introduced has preliminarily assessed system performance. Further experimentation has been conducted with real fMRI time series. Rigid-body transformations have been retrieved automatically and the values of the motion parameters compared to those obtained by the Statistical Parametric Mapping (SPM99), and the Automatic Image Registration (AIR 3.08). Results indicated that AUTOALIGN offers subvoxel accuracy in correcting both misalignment and intensity among time points in fMR images time series, and also that its performance is comparable to that of SPM99 and AIR3.08

Obtaining foot bone structure applying global and adaptive thresholding

La descripción del comportamiento mecánico de tejidos duros mediante el empleo de modelos discretos pasa por diferentes etapas de análisis, que van desde el procesamiento digital de la imagen hasta la especificación de las propiedades físicas del tejido al modelo discreto. Para lograr un buen resultado es esencial la descomposición de esos modelos en sus partes constitutivas. En este trabajo se discute un método para la descripción geométrica de los huesos del pie a partir de una secuencia de imágenes (cortes) de tomografía computarizada (TC). La investigación propone la combinación de la umbralización global y de la adaptativa para la determinación del dominio geométrico de los huesos en cada corte, así como el análisis de las relaciones espaciales entre contornos en planos consecutivos a fin de obtener las isosuperficies de los huesos. Se propone un algoritmo semiautomático basado en 4 etapas: la lectura de los cortes de imágenes de TC; la determinación de los contornos que definen el tejido óseo presentes en cada corte; la formación de los volúmenes a través del agrupamiento de los contornos cuya relación espacial cumple un criterio determinado; y la eliminación de las isosuperficies no válidas. Como resultado se obtiene la definición de la mayoría de los huesos del pie cuyo rango de valores en la escala de Hounsfield es [–1.000; 1.383].The description of the mechanical behavior of hard tissues by means of discrete models goes through various stages of analysis, which range from digital image processing to the specification of tissues physical properties to the discrete model. To achieve good results it is essential to decompose these models into their constituent parts. In this paper we discuss a method for geometrical description of foot bones from a sequence of computed tomography (CT) images. This research proposes a combination between global and adaptive thresholdings to determine the geometric domain of bones in each slice and the analysis of the spatial relationships between contours in consecutive planes in order to obtain bones’ isosurfaces. The algorithm proposed is based on 4 stages: the reading of computed tomography (CT) images; the determination of the contours that define the bone tissue present on each slice; the grouping of contours whose relationship meet a given criteria; the elimination of non-valid volumes. As a result, it is possible to obtain the geometrical domain of a great number of foot bones whose range in the Hounsfield is [–1000; 1383].Peer Reviewe

Automated Region Growing for Segmentation of Brain Lesion in Diffusion-weighted MRI

This paper presents an automatic segmentation of brain lesions from diffusion-weighted magnetic resonance imaging (DW-MRI or DWI) using region growing approach. The lesions are acute infarction, haemorrhage, tumour and abscess. Region splitting and merging is used to detect the lesion region. Then, histogram thresholding technique is applied to automate the seeds selection. The region is iteratively grown by comparing all unallocated neighbour pixels to the seeds. The difference between pixel’s intensity value and the region’s mean is used as the similarity measure. Evaluation is made for performance comparison between automatic and manual seeds selection. Overall, automated region growing algorithm provides comparable results with the semi-automatic segmentation

Métodos de segmentação para modelação 3D do ouvido a partir de imagens

O objectivo principal deste artigo centra-se na apresentação de métodos de segmentação de imagem adequados para a construção de modelos geométricos 3D das estruturas do ouvido a partir de imagens médicas de Tomografia Computorizada (TC), sendo discutidas as vantagens e desvantagens de cada um. Os métodos são classificados de acordo com as técnicas utilizadas; nomeadamente, em métodos de thresholding, de clustering e de modelos deformáveis. Neste artigo, são também apresentados e discutidos resultados experimentais de segmentação das estruturas do ouvido em imagens de TC

Medical diagnosis using NIR and THz tissue imaging and machine learning methods

The problem of extracting useful information for medical diagnosis from 2D and 3D optical imaging experimental data is of great importance. We are discussing challenges and perspectives of medical diagnosis using machine learning analysis of NIR and THz tissue imaging. The peculiarities of tissue optical clearing for tissue imaging in NIR and THz spectral ranges aiming the improvement of content data analysis, methods of extracting of informative features from experimental data and creating of prognostic models for medical diagnosis using machine learning methods are discussed

ADVANTAGES OF USING SIFT FOR BRAIN TUMOR DETECTION

The brain is the anterior most part of the central nervous system. The cranium, a bony box in the skull protects it. Virtually every activity or thought of ours is controlled by our brain. So, it’s very dangerous when the proper functioning of the brain is hindered. Brain tumor is one such disease which if not detected early and treated accordingly, can prove fatal. Structure of the brain is quite complex and hence it is very difficult to detect the abnormalities in early stages. In our paper we will be giving an overview of the various techniques used for brain tumor detection and how SIFT overcomes their limitations. The techniques discussed include biopsy, manual segmentation, mathematical morphology & wavelet transform, artificial neural network and finally SIFT (Scale Invariant Feature Transform). Biopsy is a surgical method which needs to be performed by highly skilled professionals. The rest other methods use MRI images and thus are non-invasive. SIFT technique which we are using in our project gives good accuracy, is cost effective and most importantly is invariant to translation, scale, rotation, affine transform, change in illumination, etc