Medical Image Segmentation and Classification Using MKFCM and Hybrid Classifiers

Satheesh, K.; Raj, Alex

doi:10.22266/ijies2017.1231.02

Cited by 8 publications

(6 citation statements)

References 16 publications

(14 reference statements)

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“…Satheesh, and A.N.J. Raj, [17] developed a new methodology: Orthogonal Learning Particle Swarm Optimization (OLPSO) technique for tuberculosis detection in lung image. This learning algorithm was used to identify the specific set of features from the image and ranks the features based on decision task equation.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Clustering Algorithm Efficient Implementation Using Centre of Centres

Ramesh¹,

Rao²,

Jena³

2018

IJIES

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Clustering is a procedure of finding similar data items (patterns, documents etc.) and then group the similar data together. Items belongs to different clusters are dissimilar data items, generally cluster values are considered as 1 or 0. The clustering process is not appropriate for all the cases sometimes these values are less than one. In practical situations clusters are not crisp, then it is represented as fuzzy. In order to enhance the clustering rate, two appropriate clustering approaches: K-means clustering and Fuzzy C Means (FCM) are considered. These approaches are modified by minimizing the objective function known as squared error function. The experimental research was performed on the publicly available database (i.e. yeast dataset) to validate its clustering performance in terms of accuracy, specificity, sensitivity and execution time. Experimental outcome shows that the proposed technique improves the accuracy in clustering rate up to 1.5-35% compared to the existing methodologies FCM and k-means approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Fuzzy Clustering Algorithm Efficient Implementation Using Centre of Centres

Ramesh¹,

Rao²,

Jena³

2018

IJIES

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“…Satheesh et al [26] have proposed a multiple kernel fuzzy c-means (MKFCM) algorithm for early detection of Tuberculosis (TB). In their approach, hybrid classifier is an integration of Support Vector Machine (SVM) and Artificial Neural Network (ANN) which are applied to computed tomography (CT) scan lung images to provide results with high accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Crossbred Approach for Effective Brain Stroke Lesion Segmentation

Melingi¹,

Vivekanand²

2018

IJIES

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An Ischemic stroke is expressed as lost neurological brain work because of the sudden loss of blood dissemination in the specific territory of the brain. The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. In this paper we utilize a hybrid way to deal with detecting the ischemic stroke from the alternate pathologies in magnetic resonance (MR) images utilizing Kernelized Fuzzy C-means (KFCM) clustering with adaptive threshold algorithm and the Support Vector Machine (SVM) classifier. In the existing method, the Otsu's method incorporated with SVM classifier method is utilized for the segmentation of the ischemic stroke image, but it has the limited accuracy 88%, specificity 66% and the sensitivity value is 94%. For the exact identification and segmentation the KFCM algorithm is utilized. The distance and the intensity of the lesion tissue is identified by this method. The accuracy and segmentation aftereffects of the classifier are measured in the testing and training phase by looking at the comparable and a decent variety of sample sets by considering diverse groupings. Our test comes about demonstrating that, the performance of the proposed technique is assessed in view of the precision, recall, sensitivity, accuracy and overlap metrics of the framework. Compared with the existing classification method, the proposed method has 17.64% RMSE (Root Mean Square Error), 6.24% MAPE (Mean Absolute Percentage Error) and 2.55% MBE (Mean Bias Error), consumption time is 6.45 (s) and also the sensitivity and the accuracy ranges are 98.8% and 99%. The proposed approach is actualized using MATLAB and the realtime datasets are used for our examination.

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“…The accuracy performance achieved up to 98.9%. In medical images, classification also can be used to detect tuberculosis diseases early by using hybrid classification between Artificial Neural Network (ANN) and Support Vector Machine (SVM)-the system achieves a sensitivity performance of 89.87% [2]. The Otsu method was also used in medical image processing to find the position of the vein for injection process.…”

Section: Introductionmentioning

confidence: 99%

Partial Centroid Contour Distance (PCCD) in Mango Leaf Classification

Prasetyo

Adityo

Suciati

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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The research in the classification of mango leaf varieties requires appropriate features and classification methods to achieve high accuracy. The system used 263 features, texture and color features included Boundary Moments features that generated from Centroid Contour Distances (CCD). The CCD measures distance from center to the edge along 360 degrees, this causes enormous computational loads. On the other hand, the final part of mango leaf to recognize the mango varieties simply by observing the leaf base and leaf tip, so the mango leaf as the special case of CCD can be solved by only generating features at these parts. We propose Partial CCD (PCCD) by calculating the distance from boundary point does not to the center point of the leaf but to the midpoint-cut of the leaf base or leaf tip. PCCD has two parts, PCCD Leaf Base and PCCD Leaf Tip to capture leaf base and leaf tip features, respectively. On experiment testing with PCCD or another color, shape, and texture features only, the system can't achieve high accuracy, but the combination of all features increase accuracy up to 10%. The comparison among all various features are used in classification. It is compared the original features, individual PCCD features (Leaf base and Leaf tip), and combination of Leaf base and Leaf tip. These results show that combination of original features and PCCD features achieve the best accuracy 80.17% and average accuracy 78.41%. The highest accuracy performance obtained by SVM classification is 81.73%. The comparison with other features also proved that the combination obtains better performance.

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Medical Image Segmentation and Classification Using MKFCM and Hybrid Classifiers

Cited by 8 publications

References 16 publications

Fuzzy Clustering Algorithm Efficient Implementation Using Centre of Centres

Fuzzy Clustering Algorithm Efficient Implementation Using Centre of Centres

A Crossbred Approach for Effective Brain Stroke Lesion Segmentation

Partial Centroid Contour Distance (PCCD) in Mango Leaf Classification

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