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A hybrid weighted fuzzy approach for brain tumor segmentation using MR images

  • S.I. : Deep Neuro-Fuzzy Analytics in Smart Ecosystems
  • Published:
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Abstract

Human brain tumor detection and classification are time-consuming however vital tasks for any medical expert. Assistance via computer aided diagnosis is commonly used to enhance diagnosis capabilities attaining maximum detection accuracy. Despite significant research, brain tumor segmentation is still an open challenge due to variability in image modality, contrast, tumor type, and other factors. Many great works ranging from manual, semiautomatic, or fully automatic tumor segmentation with magnetic resonance (MR) brain images are available, however, still creating a space for developing efficient and accurate approaches in this domain. This manuscript proposes a hybrid weighted fuzzy k-means (WFKM) brain tumor segmentation algorithm using MR images to retrieve more meaningful clusters. It is based on fuzzification of weights which works on spatial context with illumination penalize membership approach which helps in settling issues with pixel’s multiple memberships as well as exponential increase in number of iterations. The segmented image is further utilized for successful tumor type identification as benign or malignant by means of SVM. Experimentation performed on MR images using Digital Imaging and Communications in Medicine (DICOM) dataset shows that fusion of proposed WFKM and SVM outperforms many existing approaches. Further, performance evaluation parameters show that the proposal produces better overall accuracy. Results on variety of images further prove applicability of the proposal in detecting ranges and shapes of brain tumor. The proposed approach excels qualitatively as well as quantitatively reporting an average accuracy of 97% on DICOM dataset with total number of images varying from 100 to 1000.

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Notes

  1. Accuracy, Sensitivity, Specificity, Dice coefficient index and confusion matrix to define TP, TN, FP, and FN:

    $$\begin{aligned} \mathrm{Accuracy} =&\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{FP}+\mathrm{TN}+\mathrm{FN}} * 100 \end{aligned}$$
    (17)
    $$\begin{aligned} \mathrm{Sensitivity} =&\frac{TP}{\mathrm{TP}+\mathrm{FN}} * 100 \end{aligned}$$
    (18)
    $$\begin{aligned} \mathrm{Specificit}y =&\frac{\mathrm{TN}}{\mathrm{TN}+\mathrm{FP}} * 100 \end{aligned}$$
    (19)
    $$\begin{aligned} \text { Average dice coefficient index} =&\frac{2\mathrm{TP}}{2\mathrm{TP}+\mathrm{FP}+\mathrm{FN}} * 100 \ \ \ \end{aligned}$$
    (20)

    where

    $$\begin{aligned} \mathrm{TP}= & {} \frac{\text {Number of tumorous MR brain images}}{\text {Total number of images in dataset}} \end{aligned}$$
    (21)
    $$\begin{aligned} \mathrm{TN}= & {} \frac{\text {Number of non-tumorous MR brain images}}{\text {Total number of images in dataset}} \end{aligned}$$
    (22)
    $$\begin{aligned} \mathrm{FP}= & {} \frac{\text {No. of non-tumorous MR images detected as tumorous}}{\text {Total number of images in dataset}} \end{aligned}$$
    (23)
    $$\begin{aligned} \mathrm{FP}= & {} \frac{\text {No. of tumorous MR images detected as non-tumorous}}{\text {Total number of images in dataset}}\ \end{aligned}$$
    (24)

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  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India

    Prabhjot Kaur Chahal & Shreelekha Pandey

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  1. Prabhjot Kaur Chahal
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Correspondence to Prabhjot Kaur Chahal.

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Chahal, P.K., Pandey, S. A hybrid weighted fuzzy approach for brain tumor segmentation using MR images. Neural Comput & Applic 35, 23877–23891 (2023). https://doi.org/10.1007/s00521-021-06010-w

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