In this paper, color image segmentation based on fuzzy logic has been studied. Anefficient Fuzzy logic inference engine based on magnetic fields has been implemented in this project so as to intelligently extract color information in the given image and classify it into the predominant color pyramid with the help of artificial color magnets. This also forms the basis for the priority based edge detection. Sobel operator is used in this project which is intelligently fused with the result of the above mentioned method to enhance the output so as to obtain a priority based enhanced edge detection output. 
Experimental results have demonstrated the effectiveness and superiority of the proposed method after extensive set of color images was tested.


Fuzzy Magnetic Color Image Segmentation and Advanced Edge Detection

This paper presents the description on Healthcare organizations are required to focus on Total quality improve:- Rendering acceptable, quality health services to patients at affordable price within reasonable price, within in a reasonable time; Applying zero errors to all patients services; maintaining a continuous error prevention program; Training employees in medical care on such aspects as error prevention,reducing delay time and providing prompt reasonable to patients needs; management system have always improvement in such systems to realize the true nature of the quality of healthcare and to be motivated towards improving this quality.In spite of billions of dollars of money spent worldwide, most of the healthcare is seen to be ineffective, inefficient and inadequate. Therefore there is a crying need to bring about a paradigm shift in the quality of health care delivery and to monitor and sustain it. It is obvious that those institutions, which are quality conscious and are committed to continuous quality improvement, will gain the highest consumer acceptance and will flourish at the expense of others. The 'Quality Revolution', as it is sometimes referred to, is nothing but putting the patient at the heart of health care and wrapping the care around it, rather than the other way around. Quality Measuring and assessing service is important, particularly when multiple sources of variation are present. Analyzing all medical processes to remove rework and waste could build health care quality and lead to significant reductions in patient cost. The use of quality-assurance programs and statistical tools can be directly applied to health-care organizations with improved quality of patient's objectives and the results of care from the patient's viewpoint.

Total Quality Management in Healthcare

Segmentation of the left ventricle is important in assessment of cardiac functional parameters. Currently, manual segmentation is the gold standard for acquiring these parameters and can be time-consuming. Therefore, accuracy and automation are two important criteria in improving cardiac image segmentation methods. In this paper, we present a comprehensive approach that utilizes various features of cine MR images and combines multiple image processing methods including thresholding, edge detection, mathematical morphology, deformable model as well as image filtering. The segmentation is performed automatically with minimized interaction to optimize segmentation results. This approach provides cardiac radiologists a practical method for accurate segmentation of the left ventricle.

Segmentation of the Left Ventricle from Cine MR Images Using a Comprehensive Approach

This document describes an implementation of an alternative bilateral filtering algorithm in ITK.
This class implements the fast bilateral filter algorithm in ITK by taking the input image and organizing it into the required data structure as an ITK Image with dimension one greater than the original image. The DiscreteGaussianImageFilter is then used to blur the higher dimensional image. The discrete Gaussian image filter was chosen because the kernel width used should usually be small due to the down-sampling involved when using this technique. As described in the ITK documentation, this class should outperform the recursive Gaussian algorithm due to the small kernel size DiscreteGaussianImageFilter.After the Gaussian blur has been completed the data will be interpolated and written to the output image.

By incorporating this class into the ITK framework, this more efficient algorithm will be made available to all ITK users.

A Fast Approximation to the Bilateral Filter for ITK

This document presents a set of classes to read a PTX file (a common file format produced by LiDAR scanners) into an itkMesh object. We propose these classes as addition to the Insight Toolkit ITK (www.itk.org).

Reading a PTX file into an itkMesh

Gross shape measures such as volume have been widely used in statistical analysis of anatomical structures. Statistical shape analysis methods have emerged within the last decade to allow for a localized analysis of shape. Most shape analysis frameworks are though lacking a good statistical underpinning, as they commonly do not allow for the inclusion of independent variables such as age, gender or clinical
scores. This work presents a unified method for local shape analysis that can accomodate different number of variates and contrasts. It also allows to include any number of associated variables in the statistical analysis of the data. Several cases of study are given to clarify the explanation of the different types of data that can be analyzed and the parameters that can be used to tune the program shapeAnalysisMANCOVA. This tool has been designed to interact seamlessly with the existing UNC SPHARM-PDM based shape analysis toolbox.

Local Shape Analysis using MANCOVA

In recent years, Light Detection and Ranging (LiDAR) scanners have become more prevalent in the scientific community. They capture a "2.5-D" image of a scene by sending out thousands of laser pulses and using time-of-flight calculations to determine the distance to the first reflecting surface in the scene. Rather than setting up a collection of objects in real life and actually sending lasers into the scene, one can simply create a scene out of 3d models and "scan" it by casting rays at the models. This is a great resource for any researchers who work with 3D model/surface/point data and LiDAR data. The synthetic scanner can be used to produce data sets for which a ground truth is known in order to ensure algorithms are behaving properly before moving to "real" LiDAR scans. Also, noise can be added to the points to attempt to simulate a real LiDAR scan for researchers who do not have access to the very expensive equipment required to obtain real scans.

A Synthetic LiDAR Scanner for VTK

Many neuroanatomy studies rely on brain tissue segmentation in Magnetic Resonance images (MRI). The Expectation-Maximization (EM) theory offers a popular framework for this task. We studied the EM algorithm developed at the Surgical Planning Laboratory (SPL) at Harvard Medical School and implemented in the Slicer3 software. We observed that the segmentation lacks accuracy if the image exhibits some intensity inhomogeneity. Moreover the optimum parameters are challenging to estimate. This document aims at describing our solutions within the context of statistical modeling. Our contributions range from algorithm improvements to novel representations of the statistical distribution model. First we added a bias field correction module and exposed the most significant parameters. Second we proposed a new way to select the distribution of the tissues to be segmented. Finally we designed a set of interactive tools to make the segmentation process easier and more accurate. To validate the new segmentation pipeline, we performed our experiments on MRI data and a clinical expert evaluated our results.

The source code developed for this work, i.e. the code of the MRIBiasFieldCorrection and EMSegment modules, is part of Slicer3 version 3.5 and can be downloaded by this command: svn co http://svn.slicer.org/Slicer3/trunk Slicer3

New Expectation Maximization Segmentation Pipeline in Slicer 3

This paper describes the Carotid Lumen Segmentation and Stenosis Grading Challenge, which was part of the workshop ``3D Segmentation in the Clinic: A Grand Challenge''. This workshop was part of the 12th conference on Medical Image Computing and Computer Assisted Interventions, held in London in September 2009. We introduce the purpose and background of the challenge and describe the data, reference standard and evaluation measures that were used in this challenge. We also provide the results of the data that were submitted by industrial and academic research institutes.

Carotid Lumen Segmentation and Stenosis Grading Challenge

An Insight Toolkit (ITK) algorithm for detection of spherical objects using Hough methods with voting is presented in this paper. Currently, the usage of Hough methods for detecting linear and circular elements exists for 2D images in ITK. The current work extends those filters in several ways. Firstly, the new filters operate on N-dimensional images. Secondly, they work in physical coordinates which is quite essential in medical imaging modalities. Thirdly, they are optimized (multi-threaded execution, stratified sampling etc.) for usage on large datasets and show a significant speedup even in 2D and on small images. Our implementation follows the same underlying mathematics of Hough transforms (as implemented by the 2D filters) but with some minor variations. The main variation lies in the pattern of voting that involves selecting voting regions easily and efficiently accessible to region iterators rather than cones that are difficult to generalize in higher dimensions. We include 2D example code, parameter settings and show the results generated on embryonic images of the zebrafish from optical microscopy.