Segmentation Of Lung Structures With Fuzzy Clustering Algorithm

GRD Journals | Global Research and Development Journal for Engineering | International Conference on Innovations in Engineering and Technology (ICIET) - 2016 | July 2016

e-ISSN: 2455-5703

Segmentation of Lung Structures with Fuzzy Clustering Algorithm 1Dr.

A. Umarani 2R. Arunjunai Rani 3Su. Raja Subhashini 1 Assistant Professor 2Student 1,2,3 Department of Electronics and Instrumentation 1,2,3 K.L.N College of Engineering Abstract

Cancer are considered to be the major health threat in several regions of the world. After HIV, it is the second foremost infectious disease in worldwide causing death. When it is left undiagnosed and untreated, humanity rates of patients are high. The diagnostic methods are slow and still unreliable to detect. In order to reduce the liability of the disease, this work presents our automatic methodology for identifying Cancer. Initially, the extraction of the lung region is done using a graph cut segmentation method.

Using this lung region, we figure out a set of texture and shape features, which enable the X-rays to be classified as normal or abnormal using the SVM classifier. This paper presents a simplified methodology using fuzzy logic segmentation from the natural image processing to lung segmentation tasks over GC segmentation. The proposed indicative system for analyzing CANCER segmentation achieves a better performance than the approaches of graph cut segmentation Keyword- Segmentation, Lung Structures, Fuzzy Clustering __________________________________________________________________________________________________

I. INTRODUCTION Lung cancer seems to be the common cause of death among people throughout the world. Early detection of lung cancer can increase the chance of survival among people. The overall 5-year survival rate for lung cancer patients increases from 14 to 49% if the disease is detected in time. Although Computed Tomography (CT) can be more efficient than X-ray. However, problem seemed to merge due to time constraint in detecting the present of lung cancer regarding on the several diagnosing method used.

Hence, a lung cancer detection system using image processing is used to classify the present of lung cancer in a CT- images. In this study, MATLAB have been used through every procedures made. In image processing procedures, process such as image preprocessing, segmentation and feature extraction have been discussed in detail. We are aiming to get the more accurate results by using various enhancement and segmentation technique. Computers excel in quantitative assessment of images and computational analysis is therefore important in order to analyse large amounts of data and to aid radiologists and researchers with better quantitative, time-saving and objective measures. The image segmentation approaches can be divided into four categories: thresholding, clustering, edge detection and region extraction. In this paper, a Fuzzy based clustering method for Image segmentation will be considered and compared with the graph cut approach Routing.

II. EXISTING METHODOLOGY

Fig. 1

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Segmentation of Lung Structures with Fuzzy Clustering Algorithm (GRDJE / CONFERENCE / ICIET - 2016 / 057)

A. Extraction of the lung region The extraction of the lung region from the scan images is the first step in the studied system. Some fundamental processing techniques are used here.

B. Pre-Processing In the pre-processing process, image segmentation is done. Segmentation is partitioning or separating an image in different regions.

The segmentation is based on several features in the image. The aim of image segmentation is to group pixels into relevant image regions and it could be used to recognize the object, to estimate within motion, image editing and compression. Segmentation is an essential method in analysis of a medical image. The segmentation method is used for grouping or separating a particular region in an image, as there are few defined regions within the image.

C. Graph cut segmentation Image segmentation has come a long way. Using just a few simple grouping cues, one can now produce rather impressive segmentation on a large set of images. Behind this development, a major converging point is the use of graph based technique.

Graph cut provides a clean, flexible formulation for image segmentation. It provides a convenient language to encode simple local segmentation cues, and a set of powerful computational mechanisms to extract global segmentation from these simple local (pairwise) pixel similarities. Computationally graph cuts can be very efficient.

1) General graph cut framework for image segmentation: Normalized Cuts, Typical Cuts, and Min Cuts; 2) data human image segmentation, and segmentation benchmark; 3) image statistics and grouping cues: intensity, texture; 4) multi-scale graph cut.

Graph cuts methods have become popular alternatives to the level set-based approaches for optimizing the location of a contour.

D. Feature Extraction Features estimated for separated nodule of given sample image has been found as follows: 1) Area: It is the simplest property and by its given size. Therefore, it is the total number of white pixels in the extracted area. 2) Perimeter: It is another simple property defined by the perimeter of the region. It is the length of extracted ROI boundary.

E. Eccentricity It is used to decide the shape or circularity of the object. Area: 2291 Perimeter: 221 Eccentricity: 0.8289.

F. Classification Support vector machines are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification. The basic SVM takes a set of input data and for each given input, predicts which of two classes forms the input, making it a non-probabilistic binary linear classifier. SVM uses a kernel function which maps the given data into a different space; the separations can be made even with very complex boundaries. The different types of kernel function include polynomial, RBF, quadratic, Multi-Layer Perceptron (MLP). Each kernel is formulated by its own parameters like Îł, Ďƒ, etc. Figure 3.2.5 shows maximum margin hyper planes. The original hyper plane algorithm is a way to create non linear classifier by applying the kernel trick to maximum margin hyper phase.

III. PROPOSED METHODOLOGY

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Segmentation of Lung Structures with Fuzzy Clustering Algorithm (GRDJE / CONFERENCE / ICIET - 2016 / 057)

For our experiments, we use three CXR sets. On the first two sets we train and test our classifiers, and on the third set we train our lung models. The images usedin this study were de-identified by the data providers and are exempted from IRB review at their institutions.

A. Image Pre-processing It suppresses the noise or other small fluctuations in the image; equivalent to the suppressions of high frequencies in the frequency domain. Smoothing also blurs all sharp edges that bear important information about the image. To remove the noise from the images, median filtering is used. Median filtering is a non-linear operation often used in image processing to reduce salt and pepper noise. In general, the median filter allows a great deal of high spatial frequency detail to pass while remaining very effective at removing noise on images where less than half of the pixels in a smoothing neighbourhood have been affected.

B. Histogram of gradients (HOG) It is a descriptor for gradient orientations weighted according to gradient magnitude. The image is divided into small connected regions, and for each region a histogram of gradient directions or edge orientations for the pixels within the region is computed.

The combination of these histograms represents the descriptor. HOG has been successfully used in many detection systems.

C. Local binary pattern (LBP) It is a texture descriptor that codes the intensity differences between neighbouring pixels by a histogram of binary patterns . LBP is thus a histogram method in itself. The binary patterns are generated by thresholding the relative intensity between the central pixel and its neighbouring pixels.

D. Edge detection It is the name for a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges. The same problem of finding discontinuities in 1D signal is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction. The purpose of detecting sharp changes in image brightness is to capture important events and changes in properties of the world. It can be shown that under rather general assumptions for an image formation model.  Discontinuities in depth,  Discontinuities in surface orientation,  Changes in material properties and  Variations in scene illumination E. Classification To detect abnormal CXRs with cancer, we use a support vector machine (SVM), which classifies the computed feature vectors into either normal or abnormal. An SVM in its original form is a supervised no probabilistic classifier that generates hyper planes to separate samples from two different classes in a space with possibly infinite dimension. The unique characteristic of an SVM is that it does so by computing the hyper plane with the largest margin; i.e., the hyper plane with the largest distance to the nearest training data point of any class. Ideally, the feature vectors of abnormal CXRs will have a positive distance to the separating hyper plane, and feature vectors of normal CXRs will have a negative distance.

IV. FUZZY CLUSTERING Clustering is a process for classifying objects or patterns in such a way that samples of the same group are more similar to one another than samples belonging to different groups. Many clustering strategies have been used, such as the hard clustering scheme and the fuzzy clustering scheme, each of which has its own special characteristics. The conventional hard clustering method restricts each point of the data set to exclusively just one cluster. As a consequence, with this approach the segmentation results are often very crisp, i.e., each pixel of the image belongs to exactly just one class. However, in many real situations, for images, issues such a limited spatial resolution, poor contrast, overlapping intensities, noise and intensity in homogeneities variation make this hard (crisp) segmentation a difficult task. The fuzzy set theory was proposed, which produced the idea of partial membership of belonging described by a membership function; fuzzy clustering as a soft segmentation method has been widely studied and successfully applied in image segmentation. Among the fuzzy clustering methods, fuzzy c-means (FCM) algorithm is the most popular method used in image segmentation because it has robust characteristics for ambiguity and can retain much more information than hard segmentation methods.

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Segmentation of Lung Structures with Fuzzy Clustering Algorithm (GRDJE / CONFERENCE / ICIET - 2016 / 057)

V. RESULT ANALYSIS

Fig. 3 Input Image

Color Image

Fig. 4: Fuzzy Clustering

Fig. 5: Before & After Applying Histogram Equalisation

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Segmentation of Lung Structures with Fuzzy Clustering Algorithm (GRDJE / CONFERENCE / ICIET - 2016 / 057)

Fig. 6: Segmentation Normal image

Abnormal image

Fig. 7: Edge Detection Normal image

Abnormal image

Fig. 8: Morphological Operation Normal image

Abnormal image

VI. CONCLUSION In this work, a method for automatic segmentation of the lungs from chest CT scans using graph cut segmentation and Fuzzy based segmentation have been presented. The method incorporates spatial prior knowledge, intensity modelling and neighbourhood

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Segmentation of Lung Structures with Fuzzy Clustering Algorithm (GRDJE / CONFERENCE / ICIET - 2016 / 057)

modelling into a graph cut segmentation framework. The method serves as an alternative to time-consuming and subjective manual descriptions by providing an automated and objective segmentation.

This paper presents a fuzzy method for image segmentation. Although the method for image segmentation based on fuzzy logic is sufficient, in future efficient methods can be develop for image segmentation. It can offer more accurate result. As lung segmentation is a prerequisite for further computational analysis of the lungs, the obtained segmentation may serve as a basis for quantitative analysis of the lungs.

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