Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering

Abstract

Suspicious lesion or organ segmentation is a challenging task to be solved in most of the medical image analyses, medical diagnoses and computer diagnosis systems. Nevertheless, various image segmentation methods were proposed in the previous studies with varying success levels. But, the image segmentation problems such as lack of versatility, low robustness, high complexity and low accuracy in up-to-date image segmentation practices still remain unsolved. Fuzzy c-means clustering (FCM) methods are very well suited for segmenting the regions. The noise-free images are effectively segmented using the traditional FCM method. However, the segmentation result generated is highly sensitive to noise due to the negligence of spatial information. To solve this issue, super-pixel-based FCM (SPOFCM) is implemented in this paper, in which the influence of spatially neighbouring and similar super-pixels is incorporated. Also, a crow search algorithm is adopted for optimizing the influential degree; thereby, the segmentation performance is improved. In clinical applications, the SPOFCM feasibility is verified using the multi-spectral MRIs, mammograms and actual single spectrum on performing tumour segmentation tests for SPOFCM. Ultimately, the competitive, renowned segmentation techniques such as k-means, entropy thresholding (ET), FCM, FCM with spatial constraints (FCM_S) and kernel FCM (KFCM) are used to compare the results of proposed SPOFCM. Experimental results on multi-spectral MRIs and actual single-spectrum mammograms indicate that the proposed algorithm can provide a better performance for suspicious lesion or organ segmentation in computer-assisted clinical applications.

Keywords: Breast MR images; Clinical application; Fuzzy C-means; Mammograms; Spatial; Super-pixel.

Fig. 1

Example of the super-pixel segmentation with different window sizes. a Original breast MRI image with medium size tumour. b Super-pixel segmentation with 500 super-pixels. c Super-pixel segmentation with 1000 super-pixels

Fig. 2

Chosen of neighbouring and similar super-pixel

Fig. 3

Illustration of the proposed segmentation approach

Fig. 4

Clustering results of real MR brain image: it is obvious to show that the SPOFCM method provides almost noise-free segmented image, i.e. with almost zero misclassified pixels

Fig. 5

The breast MRIs including the tumour section images captured under different sequences in actual cases (T1_FS fat-saturated T1-weighted, T2_FS fat saturated T2-weighted, PD_FS fat-saturated PD-weighted, FLASH fast low-angle shot)

Fig. 6

Segmented results obtained using various segmentation methods on CEM images

Fig. 7

The objective function value of our method for segmenting three mammogram images (three cases)

Fig. 8

Convergence graph for CSA vs PSO and GA

Fig. 9

Results of tumour ROIs designated in mammograms obtained by different segmentation algorithms, where the standard was manually depicted by experts

Fig. 9

Results of tumour ROIs designated in mammograms obtained by different segmentation algorithms, where the standard was manually depicted by experts