3D medical volume segmentation using hybrid multiresolution statistical approaches

This article is available through the Brunel Open Access Publishing Fund. Copyright © 2010 S AlZu’bi and A Amira.3D volume segmentation is the process of partitioning voxels into 3D regions (subvolumes) that represent meaningful physical entities which are more meaningful and easier to analyze and usable in future applications. Multiresolution Analysis (MRA) enables the preservation of an image according to certain levels of resolution or blurring. Because of multiresolution quality, wavelets have been deployed in image compression, denoising, and classification. This paper focuses on the implementation of efficient medical volume segmentation techniques. Multiresolution analysis including 3D wavelet and ridgelet has been used for feature extraction which can be modeled using Hidden Markov Models (HMMs) to segment the volume slices. A comparison study has been carried out to evaluate 2D and 3D techniques which reveals that 3D methodologies can accurately detect the Region Of Interest (ROI). Automatic segmentation has been achieved using HMMs where the ROI is detected accurately but suffers a long computation time for its calculations

Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation

Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86

Enhancement of Image Resolution by Binarization

Image segmentation is one of the principal approaches of image processing. The choice of the most appropriate Binarization algorithm for each case proved to be a very interesting procedure itself. In this paper, we have done the comparison study between the various algorithms based on Binarization algorithms and propose a methodologies for the validation of Binarization algorithms. In this work we have developed two novel algorithms to determine threshold values for the pixels value of the gray scale image. The performance estimation of the algorithm utilizes test images with, the evaluation metrics for Binarization of textual and synthetic images. We have achieved better resolution of the image by using the Binarization method of optimum thresholding techniques.Comment: 5 pages, 8 figure

Quantification of sub-resolution porosity in carbonate rocks by applying high-salinity contrast brine using X-ray microtomography differential imaging

Characterisation of the pore space in carbonate reservoirs and aquifers is of utmost importance in a number of applications such as enhanced oil recovery, geological carbon storage and contaminant transport. We present a new experimental methodology that uses high-salinity contrast brine and differential imaging acquired by X-ray tomography to non-invasively obtain three-dimensional spatially resolved information on porosity and connectivity of two rock samples, Portland and Estaillades limestones, including sub-resolution micro-porosity. We demonstrate that by injecting 30 wt% KI brine solution, a sufficiently high phase contrast can be achieved allowing accurate three-phase segmentation based on differential imaging. This results in spatially resolved maps of the solid grain phase, sub-resolution micro-pores within the grains, and macro-pores. The total porosity values from the three-phase segmentation for two carbonate rock samples are shown to be in good agreement with Helium porosity measurements. Furthermore, our flow-based method allows for an accurate estimate of pore connectivity and a distribution of porosity within the sub-resolution pores

MRI image segmantation based on edge detection

Cílem této práce je představit základní segmentační techniky používáné v oblasti medicínského zpracování obrazových dat a pomocí 3D prohlížeče schopného zobrazit 3D obrazy implementovat segmentační modul založený na hranové detekci a vyhodnotit výsledky. Navrhovaný prohlížeč je sestavený v prostředi Matlab GUI a je schopen načíst objem 3D snímků představující lidskou hlavu. Navrhovaný segmentační modul je založen na použití hranových detektorů, zejména Cannyho detektoru.The aim of this thesis is to present the basic segmentation techniques uses in the field of medical image processing and by using a 3D viewer able to visualize 3D images, implement a segmentation module based on edges detection and evaluate the results. The proposed viewer is a 3D viewer build using matlab GUI and is able to load a volume of images representing the human head. The proposed segmentation module is based on the use of edge detectors particularly the Canny algorithm.