Tamalika Chaira

Intuitionistic Fuzzy Segmentation of Medical Images

This paper proposes a novel and probably the first method, using Attanassov intuitionistic fuzzy set theory to segment blood vessels and also the blood cells in pathological images. This type of segmentation is very important in detecting different types of human diseases, e.g., an increase in the number of vessels may lead to cancer in prostates, mammary, etc. The medical images are not properly illuminated, and segmentation in that case becomes very difficult. A novel image segmentation approach using intuitionistic fuzzy set theory and a new membership function is proposed using restricted equivalence function from automorphisms, for finding the membership values of the pixels of the image. An intuitionistic fuzzy image is constructed using Sugeno type intuitionistic fuzzy generator. Local thresholding is applied to threshold medical images. The results showed a much better performance on poor contrast medical images, where almost all the blood vessels and blood cells are visible properly. There are several fuzzy and intuitionistic fuzzy thresholding methods, but these methods are not related to the medical images. To make a comparison with the proposed method with other thresholding methods, the method is compared with six nonfuzzy, fuzzy, and intuitionistic fuzzy methods.

A rank ordered filter for medical image edge enhancement and detection using intuitionistic fuzzy set

This paper gives a novel scheme using intuitionistic fuzzy set theory to enhance the edges of medical images. Medical images contain lots of uncertainties, as they are poorly illuminated and fuzzy/vague in nature. So, direct segmentation techniques will not produce better results. There are lots of researches on edge enhancement starting from non-fuzzy to fuzzy set, but proper enhancement (highlighting important structures) is not obtained. Enhancement of edges helps in recovering the important structures that are not visible properly. Even minute pathological blood vessels/cells are not visible properly and in that case edge enhancement will enhance these blood vessels/cells. Intuitionistic fuzzy set theory is found suitable in medical image processing as it considers more (two) uncertainties as compared to fuzzy set theory. In the processing phase, image is initially converted to intuitionistic fuzzy image and intuitionistic fuzzy entropy is used to obtain the optimum value of the parameter in the membership and non-membership functions. Then it computes the total variation of the pixels with respect to the median value of the image window (rank order filtering). This enhances the borders or the edges of the image. The resulting image is then segmented (edge detected) using standard Cannys edge detector, when simply using Cannys edge detector does not give better result. From the result it is observed that on comparing with non-fuzzy and fuzzy methods, the proposed method gives better information about the images, which is helpful to the pathologists in accurate diagnosing of diseases.

An improved medical image enhancement scheme using Type II fuzzy set

A contrast enhancement of medical images using Type II fuzzy set theory is suggested. Fuzzy set theory considers uncertainty in the form of membership function but to have better information on uncertainty on the membership function, Type II fuzzy set is considered. Type II fuzzy set considers fuzziness in the membership function. Hamacher T co norm is used as an aggregation operator to form a new membership function using the upper and lower membership function of Type II fuzzy set. The image with the new membership function is an enhanced image. As medical images contain lot of uncertainties; Type II fuzzy set may be a good tool for medical image analysis. To show the effectiveness of the proposed method, the results are compared with fuzzy, intuitionistic fuzzy, and existing Type II fuzzy methods. To show the advantage of the proposed enhancement method, detection or extraction of abnormal lesions or blood vessels has been carried out on enhanced images of all the methods.Results on enhancement and segmentation of blood cells are shown. It is observed that the enhanced images using the proposed method are better. Also, the segmented images using the proposed enhancement method looks better where all the blood cells are clearly segmented. Type II fuzzy image enhancement scheme on medical images is proposed.It considers another uncertainty in the membership function of fuzzy set.New membership function is proposed using Hamacher T co norm.Statistical analysis of the proposed method is done with existing methods.The advantage the proposed enhancement scheme is verified using segmentation. A contrast enhancement of medical images using Type II fuzzy set theory is suggested. Fuzzy set theory considers uncertainty in the form of membership function but to have better information on uncertainty on the membership function, Type II fuzzy set is considered. Type II fuzzy set considers fuzziness in the membership function. Hamacher T co norm is used as an aggregation operator to form a new membership function using the upper and lower membership function of Type II fuzzy set. The image with the new membership function is an enhanced image. As medical images contain lot of uncertainties, Type II fuzzy set may be a good tool for medical image analysis. To show the effectiveness of the proposed method, the results are compared with fuzzy, intuitionistic fuzzy, and existing Type II fuzzy methods. Experiments on several images show that the proposed Type II fuzzy method performs better than the existing methods. To show the advantage of the proposed enhancement method, detection or extraction of abnormal lesions or blood vessels has been carried out on enhanced images of all the methods. It is observed that the segmented results on the proposed enhanced images are better.

Medical image enhancement using intuitionistic fuzzy set

In this paper, a novel image enhancement of medical images using intuitionistic fuzzy set theory is presented. In many image processing problems, preprocessing is required to improve the quality of images before processing. But for medical images, image enhancement is very useful as medical images are poorly illuminated and many of the important structures are hardly visible. Enhancement will improve the quality of the image and specifically the structures that are poorly visible, are also highlighted, thereby suitable for further processing. As intuitionistic fuzzy set takes into account more (two) uncertainties as compared to fuzzy set that considers only one uncertainty in the form of membership function, so enhancement using intuitionistic fuzzy set theory may be a good tool for medical image processing. Experiments on several medical images are performed and the results are compared with the existing fuzzy and intuitionistic enhancement methods. It is observed that the results using the proposed method are found to be quiet better.

An Atanassov's intuitionistic Fuzzy Kernel Clustering for Medical Image segmentation

AbstractThis paper suggests a novel method for medical image segmentation using kernel based Atanassovs intuitionistic fuzzy clustering. The widely used fuzzy c means clustering that uses Euclidean distance has many limitations in clustering the regions accurately. To overcome these difficulties, we introduce a new method using Atanassovs intuitionistic fuzzy set theory that incorporates a robust kernel based distance function. As the membership degrees are not precise and may contain hesitation, Sugeno type fuzzy complement is used to find the non-membership values and then hesitation degree is computed. The algorithm uses all the three kernels – Gaussian, radial basis, and hyper tangent kernels. In the algorithm, for each pixel, two features are considered - pixel energy and mean and the average of the two features are taken. The method clusters the tumors/lesions/clots almost accurately especially in a noisy environment. Experiments are performed on several noisy medical images and to assess the perf...

Accurate segmentation of leukocyte in blood cell images using Atanassov's intuitionistic fuzzy and interval Type II fuzzy set theory.

In this paper automatic leukocyte segmentation in pathological blood cell images is proposed using intuitionistic fuzzy and interval Type II fuzzy set theory. This is done to count different types of leukocytes for disease detection. Also, the segmentation should be accurate so that the shape of the leukocytes is preserved. So, intuitionistic fuzzy set and interval Type II fuzzy set that consider either more number of uncertainties or a different type of uncertainty as compared to fuzzy set theory are used in this work. As the images are considered fuzzy due to imprecise gray levels, advanced fuzzy set theories may be expected to give better result. A modified Cauchy distribution is used to find the membership function. In intuitionistic fuzzy method, non-membership values are obtained using Yagers intuitionistic fuzzy generator. Optimal threshold is obtained by minimizing intuitionistic fuzzy divergence. In interval type II fuzzy set, a new membership function is generated that takes into account the two levels in Type II fuzzy set using probabilistic T co norm. Optimal threshold is selected by minimizing a proposed Type II fuzzy divergence. Though fuzzy techniques were applied earlier but these methods failed to threshold multiple leukocytes in images. Experimental results show that both interval Type II fuzzy and intuitionistic fuzzy methods perform better than the existing non-fuzzy/fuzzy methods but interval Type II fuzzy thresholding method performs little bit better than intuitionistic fuzzy method. Segmented leukocytes in the proposed interval Type II fuzzy method are observed to be distinct and clear.

Contrast enhancement of medical images using type II fuzzy set

A novel contrast image enhancement of medical images using Type II fuzzy set theory is suggested. Hamacher T co norm is used as an aggregation operator to form a new membership function using the upper and lower membership function of Type II fuzzy set. The parameter in the Hamacher T co norm is computed from the average of the image. The image with the new membership function is an enhanced image. Medical images contain lot of uncertainties, and as Type II fuzzy set considers fuzziness in fuzzy membership function; it may be a good tool for medical image analysis. To show the effectiveness of the proposed method, the results are compared with non-fuzzy, fuzzy, intuitionistic fuzzy, and the existing Type II fuzzy methods. Experiments on several images show that the proposed Type II fuzzy method performs better than the existing methods.

Intuitionistic fuzzy color clustering of human cell images on different color models

This paper provides a color cell image clustering algorithm using intuitionistic fuzzy set theory using different color models. The clustering algorithm clusters the blood cells very clearly that helps in detecting various types of human diseases. Clustering of medical images is a challenging task as medical images are vague in nature due to poor illumination. So the boundaries or regions are not clear. Clustering using fuzzy set theory is very robust but still there is some uncertainty present while defining the membership function in fuzzy set theory. This uncertainty is due the lack of knowledge or personal error while defining the membership function. Intuitionistic fuzzy set takes into account this uncertainty and thus it may be useful in medical or real time image processing. The two uncertainty parameters in intuitionistic fuzzy set thus help in converging the cluster center to a desirable location than the cluster centers obtained by fuzzy C means algorithm. Different color models e.g., RGB, HSV, and CIELab are used in this algorithm and it is found that RGB and CIELab give almost similar result. The algorithm is also tested on conventional fuzzy C means algorithm to show the efficacy of the new algorithm.

Construction of fuzzy edge image using Interval Type II fuzzy set

AbstractIn this paper, a novel method to generate fuzzy edges in medical images using the Type II fuzzy set theory is presented. Medical images are normally poorly illuminated and many edges are not visible properly, so construction of fuzzy edge image is a difficult task. Fuzzy edges are not the binary edges but it signifies the change in intensity levels of the image. The method is based on computation of minimum and maximum values of the intensity levels of the image in a 3x3 pixel neighborhood to form two image matrices with maximum and minimum values. For better representation of uncertainty, Type II fuzzy set is applied to compute upper and lower membership levels of each image matrix. Divergence is computed between the two levels of the maximum value image matrix and also for the minimum value image matrix. Finally, difference between the divergence matrices produces an edge image. Experiment has been performed on several poorly illuminated medical images and the edges are observed to better when c...

A novel rank ordered filter based edge enhancement of medical images using intuitionistic fuzzy set theory

This paper proposes a novel method to enhance the edges of the image using intuitionistic fuzzy set theoretic approach. It uses a rank ordered filter to enhance the boundaries by calculating a total variation between the pixels in the image window and the median. If the boundaries are enhanced, then accurate edges are detected. As medical images are poorly illuminated, many important structures become hardly visible and direct edge detection techniques will not give accurate result. So, preprocessing in edge enhancement is necessary. From the edge detected image, morphological features of cells and other abnormal structures of the body can easily be computed. This method is compared with fuzzy/non-fuzzy method and it is observed that the proposed method gives better or similar result.