BLOOD VESSEL ENHANCEMENT AND SEGMENTATION USING WAVELET TRANSFORM
Submitted by
RAHUL RAJAN
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
SREE CHITRA THIRUNAL COLLEGE OF ENGINEERING, THIRUVANANTHAPURAM - 695 018.
SEPTEMBER 2010

ABSTRACT


Retinal vessel segmentation is an essential step for the diagnoses of various eye diseases. An

automated tool for blood vessel segmentation is useful to eye specialists for purpose of patient screening and clinical study. Vascul ....etc

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ABSTRACT
For languages without word boundary delimiters, dictionaries are
needed for segmenting running texts. This figure makes
segmentation accuracy depend significantly on the quality of the
dictionary used for analysis. If the dictionary is not sufficiently
good, it will lead to a great number of unknown or unrecognized
words. These unrecognized words certainly reduce segmentation
accuracy. To solve such problem, we propose a method based on
decision tree models. Without use of a dictionary, specific
information, called syn ....etc

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Presented by:
E.Malleswara Rao


Image Segmentation Using Information Bottleneck Method
The purpose of this paper is to introduce new segmentation algorithms using a hard version of the information bottleneck method. The objective of this method is to extract a compact representation of a random variable with minimal loss of mutual information with respect to another variable.
Problem Definition:
Existing system:
New segmentation algorithms using a hard version of the information bottleneck m ....etc

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Segmentation of Pathological and Healthy tissues with Reduced Weighted vectors using Hybrid Intelligence Techniques
Abstract -- The proposed Method for automatic segmentation and detection of pathological tissues, normal tissues and CSF of human brain in Magnetic resonance image (MRI) is discussed in this paper. This method is implemented in two phases, (1) the MRI brain image is acquired from patient database, In that film artifact and noise are removed. (2) In second phase, hierarchal self organizing ....etc

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Abstract
Image segmentation is an important and challenging factor in the medical image segmentation. Thispaper describes segmentation method consisting of two phases. In the first phase, the MRI brain imageis acquired from patients’ database, In that film, artifact and noise are removed after that HSom isapplied for image segmentation. The HSom is the extension of the conventional self organizing mapused to classify the image row by row. In this lowest level of weight vector, a higher value of tumorpixels, computation speed is achie ....etc

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Presented By:
Prasunjit Nayak
Sunil Kumar Swain
Debidatta Satapathy
Balaram Sabat
Avinash Kotni


Introduction
E- Commerce
Customer Segmentation
Data mining
Clustering
Soft clustering
Methodology Implemented
Dimension reduction
Using PCA
Clustering
Using C-means
Soft clustering
Fuzzy C-means
Current Status
Conclusion
....etc

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Hai folks

Can anyone please provide the project -> background subtraction in opencv using gaussian mixtures project ... i have surfed on net to know about it ... you all are helping in findind and doing semister projects succesfuully thanx to that nature. can any one tell me where can i get clear idea on my project and code helpers etc..

regards
maroom ....etc

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Presented By
N.V.L Deepak & M.Ravi


ABSTRACT:
A novel algorithm for image retrieval is presented in this paper. The basic idea of the new algorithm is that the constituent segments of the images are used to retrieve images within a digital library. Image retrieval using segments is distinctive in that the local features of the image are used to retrieve the image instead of the typically utilized global features. In our algorithm, the given image is first segmented into dominant components and then the featur ....etc

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Brain Tumor detection using Color-Based K-means Clustering Segmentation

INTRODUCTION

This paper proposes a color-based segmentation method that uses the K-means clustering technique to track tumor objects in magnetic resonance (MR) brain images. The key concept in this color-based segmentation algorithm with K-means is to convert a given gray-level MR image into a color space imag ....etc

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segmentation algorithm for medical image segmentation. The use of the conventional watershed algorithm for medical image analysis is widespread because of its advantages, such as always being able to produce a complete division of the image. However, its drawbacks include over-segmentation and sensitivity to false edges. We address the drawbacks of the conventional watershed algorithm when it is applied to medical images by using k-means clustering to produce a primary segmentation of the image before we apply our improved watershed segmentatio ....etc

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