MRI image segmentation using thresholding with 3-class C-means clustering

Khandelwal, Mohit; Shirsagar, Shraddha; Rawat, Prashant

doi:10.1109/icisc.2018.8399032

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Text Segmentation [1] Region Growing [12,13] Fuzzy connectedness [28] Graph Cut [27,28] Random Walk [28,29] Watershade [30,31] Hard Clustering [35][36][37][38] Soft Clustering [39][40][41][42] C-Means [45][46][47][48][49] K-Means [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] Fusion Based…”

Section: Machine Learningmentioning

confidence: 99%

“…ese methods compare the pixel brightnesses with a selected threshold to segment the images. e graph cuts [31], random walks [32], watershed transform [33], clustering [34,35], active contour model [18,19,36], hidden Markov random field (HMRF) [37], fuzzy entropy [38], etc. are included in unsupervised image segmentation and are mostly popular and useful for their simplicity without depending on training samples and labels.…”

Section: Snakes Basedmentioning

confidence: 99%

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Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC

Gupta

Bhadauria

2022

Computational Intelligence and Neuroscience

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During the COVID-19 pandemic, huge interstitial lung disease (ILD) lung images have been captured. It is high time to develop the efficient segmentation techniques utilized to separate the anatomical structures and ILD patterns for disease and infection level identification. The effectiveness of disease classification directly depends on the accuracy of initial stages like preprocessing and segmentation. This paper proposed a hybrid segmentation algorithm designed for ILD images by taking advantage of superpixel and K-means clustering approaches. Segmented superpixel images adapt the better irregular local and spatial neighborhoods that are helpful to improving the performance of K-means clustering-based ILD image segmentation. To overcome the limitations of multiclass belongings, semiadaptive wavelet-based fusion is applied over selected K-means clusters. The performance of the proposed SPFKMC was compared with that of 3-class Fuzzy C-Means clustering (FCM) and K-Means clustering in terms of accuracy, Jaccard similarity index, and Dice similarity coefficient. The SPFKMC algorithm gives an accuracy of 99.28%, DSC 98.72%, and JSI 97.87%. The proposed Fused Clustering gives better results as compared to traditional K-means clustering segmentation with wavelet-based fused cluster results.

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Section: Machine Learningmentioning

confidence: 99%

Section: Snakes Basedmentioning

confidence: 99%

Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC

Gupta

Bhadauria

2022

Computational Intelligence and Neuroscience

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“…It has higher accuracy than other methods such as neural network or Kmean method. FCM clustering for brain tumor gives three clusters; CSF, WM, and GM (brain tissues) [15,16]. The fuzzy partition should have two properties; homogeneity within the clusters and heterogeneity between clusters [17,18].…”

Section: Proposed Image Pre-processingmentioning

confidence: 99%

Early Classification of Brain Tumor Based on Image Histogram Using Fuzzy-Genetic Algorithm

Barakat

Elbadwey

Ibrahim

2021

Fayoum University Journal of Engineering

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A Brain Tumor Classification Framework has been outlined and created. The framework uses computer based strategies to identify tumor blocks or lesions and classify the sort of tumor utilizing matching histogram in MRI images of different patients with brain tumors. The picture processing methods such as picture segmentation, picture enhancement. Several techniques can classify the tumor such as Support vector machine (SVM), artificial neural networks (ANN), and Naive Bayes, but they did not accomplish the required accuracy. The automatic classification of tumors requires high precision since the non-accurate conclusion would cause a rise within the predominance of more serious diseases. In this paper, the proposed method using fuzzy logic and genetic algorithm based on image histogram to enhance the brain tumor classification. The experimental result showed that our technique is more effective than the previous techniques, as well as the classification accuracy efficiency is 99.9%.

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An Optimal Cluster Fusion and Morphological Segmentation Technique for IRIS Recognition

Shaikh

Khan

Singh

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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MRI image segmentation using thresholding with 3-class C-means clustering

Cited by 3 publications

References 5 publications

Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC

Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC

Early Classification of Brain Tumor Based on Image Histogram Using Fuzzy-Genetic Algorithm

An Optimal Cluster Fusion and Morphological Segmentation Technique for IRIS Recognition

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