An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images

Vishnuvarthanan, G.; Murugan, Pallikonda Rajasekaran; Subbaraj, P.; Vishnuvarthanan, Anitha

doi:10.1016/j.asoc.2015.09.016

Cited by 142 publications

(74 citation statements)

References 31 publications

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“…Once the radiofrequency signals are terminated or the energy is exhausted, the hydrogen molecules emit the absorbed energy, which helps in bringing out the images of organs. Some of the MR image sequences obtained using an MRI scanner are classified as T1‐weighted (T1‐W), T2‐weighted (T2‐W), FLAIR (fluid‐attenuated inversion recovery), and MRS (magnetic resonance spectroscopy) images …”

Section: Introductionmentioning

confidence: 99%

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

Vigneshwaran

Govindaraj

Murugan

et al. 2019

Int J Imaging Syst Tech

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Human‐made/developed algorithms provide automatic identification and segmentation of the tissues, lesions and tumor regions available in brain magnetic resonance scan images, which invocates predicaments such as high computational cost and low accuracy rate. Such hassles are reconciled with the utilization of an unsupervised approach in combination with clustering techniques. Initially, static features are chosen from the input image, which is fed to the self‐organizing map (SOM), where the algorithm employs the dimensionality reduction of input images. Consecutively, the reduced SOM prototype of data is clustered by the modified fuzzy K‐means (MFKM) algorithm. The MFKM algorithm can be modified in terms of membership variables because it operates with spatial information and converges quickly, and this would be of greater benefit to radiologists as they reduce the wrong predictions and voluminous time that normally occur owing to human involvement. The proposed algorithm provides 98.77% sensitivity and 97.5% specificity, which are better than any other traditional algorithms mentioned in this article.

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Section: Introductionmentioning

confidence: 99%

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

Vigneshwaran

Govindaraj

Murugan

et al. 2019

Int J Imaging Syst Tech

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“…A magnetic resonance imaging (MRI) scanner serves the purpose of imaging any region or organ and has the capability to produce images of the tissues along with a minimal level of bone structures present in the human body. The tumor region permeated in the human body can be easily assessed and visualized with the intervention of an MRI scanner . The MRI scanner acts as a vital tool in both the domains of clinical diagnostics and surgical operations.…”

Section: Introductionmentioning

confidence: 99%

“…MRI scanner requires a minute or less than to provide the information regarding the anatomy of human body, and its proficiency in expressing the details of anatomy of brain is widely used by the radiologists to study the characteristics of lesions, which creates convulsive irreparable effects to the brain. The lesions captured in an image by an MRI scanner are optimistically and favorably visualized by radiologists using several soft computing methodologies proposed by the researchers for decades . One such soft computing methodology in combination with an optimization technique, which is indigenous, has been briefly discussed in this article.…”

Section: Introductionmentioning

confidence: 99%

A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

Alagarsamy

Kamatchi

Govindaraj

et al. 2017

Int J Imaging Syst Tech

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This article aims at developing an automated hybrid algorithm using Cuckoo Based Search (CBS) and interval type‐2 fuzzy based clustering, so as to exhibit efficient magnetic resonance (MR) brain image segmentation. An automatic MR brain image segmentation facilitates and enables a radiologist to have a brief review and easy analysis of complicated tumor regions of imprecise gray level regions with minimal user interface. The tumor region having severe intensity variations and suffering from poor boundaries are to be detected by the proposed hybrid technique that could ease the process of clinical diagnosis and this tends to be the core subject of this article. The ability of the proposed technique is compared using standard comparison parameters such as mean squared error, peak signal to noise ratio, computational time, Dice Overlap Index, and Jaccard Tanimoto Coefficient Index. The proposed CBS combined with interval type‐2 fuzzy based clustering produces a sensitivity of 0.7143 and specificity of 0.9375, which are far better than the conventional techniques such as kernel based, entropy based, graph‐cut based, and self‐organizing maps based clustering. Appreciable segmentation results of tumor region that enhances clinical diagnosis is made available through this article and two of the radiologists who have hands on experience in the field of radiology have extended their support in validating the efficiency of the proposed methodology and have given their consent in utilizing the proposed methodology in the processes of clinical oncology.

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“…Kanas et al [7] combined the intensity clustering method with Random Walker algorithm to extract neoplastic in MR images. Likewise, the hybrid self-organizing maps with fuzzy K-means algorithm was tested by Vishnuvarthanan et al [8], and they obtained successful tumor identification in their experiments. Since nature-inspired methods became popular in image processing for optimization processes, the active contours driven by the cuckoo search (CS) strategy was introduced in Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Many efforts were made in recent years to develop human-free intervention methods that can achieve similar results to those obtained by physicians. Numerous techniques have been used for this purpose, which can be grouped in 2 classes, i.e., supervised [1][2][3][4][5][6] and unsupervised methods [7][8][9][10][11]. In Refs.…”

Section: Introductionmentioning

confidence: 99%

Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation

Ilunga-Mbuyamba

Aviña-Cervantes

Cepeda-Negrete

et al. 2017

Computers in Biology and Medicine

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A. y Chalopi, Claire (2017). Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation. Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately. Computers in Biology and

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An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images

Cited by 142 publications

References 31 publications

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation

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