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. 2018 Nov 29;19(11):3203-3209.
doi: 10.31557/APJCP.2018.19.11.3203.

Image Registration Based Cervical Cancer Detection and Segmentation Using ANFIS Classifier

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Free PMC article

Image Registration Based Cervical Cancer Detection and Segmentation Using ANFIS Classifier

B Karthiga Jaya et al. Asian Pac J Cancer Prev. .
Free PMC article

Abstract

Cervical cancer is the leading cancer in women around the world. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) classifier based cervical cancer detection and segmentation methodology is proposed. This proposed system consists of the following stages as Image Registration, Feature extraction, Classifications and Segmentation. Fast Fourier Transform (FFT) is used for image registration. Then, Grey Level Co-occurrence Matrix (GLCM), Grey level and trinary features are extracted from the registered cervical image. Next, these extracted features are trained and classified using ANFIS classifier. Morphological operations are now applied over the classified cervical image to detect and segment the cancer region in cervical images. Simulations on large cervical image dataset demonstrate that the proposed cervical cancer detection and segmentation methodology outperforms the state of-the-art methods in terms of sensitivity, specificity and accuracy.

Keywords: Cervical cancer; feature extraction; registration; classification; segmentation.

Figures

Figure 1
Figure 1
Cervical Images (a) Normal case (b) Abnormal case
Figure 2
Figure 2
Proposed Flow for Cervical Cancer Detection and Segmentation
Figure 3
Figure 3
(a) Reference cervical image (b) Source cervical image (c) Registered cervical image
Figure 4
Figure 4
Extracted Grey Level Features (a) P1(x,y) (b) P2(x,y) (c) P3(x,y) (d) P4(x,y) (e) P5(x,y)
Figure 5
Figure 5
(a) Trinary feature image1 (b) Trinary feature image 2
Figure 6
Figure 6
ANFIS Architecture for Cervical Cancer Classifications
Figure 7
Figure 7
(a) Thresholded image (b) Abnormal edges detected image (c) Cancer region segmented image
Figure 8
Figure 8
(a) Source cervical images (Available at: http://www.nccc-online.org/index.php/cervicaltumor) (b) Cancer region segmentation by proposed method (c) Ground truth images

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