Abstract
Image segmentation is the fundamental step in medical image analysis. Segmentation is a procedure to separate similar portions of images showing resemblance in different features such as color, intensity, or texture. Grayscale images are mostly used for the segmentation of medical images. Tumors are commonly stated as the abnormal growth of tissues and the brain tumor is a diseased part in the body tissues that is an abnormal mass in which the growth rate of cells is irrepressible. The mortality rate of people has raised over the past years due to brain tumors, hence this area has gained the attention of researchers. Automatic detection of brain tumors is a challenging task because it involves pathology, functional physics of MRI along with intensity and shapes analysis of MR image. After all, tumor shape, size, location, and intensity vary for each infected case. In this work, a novel hybrid approach is implemented by combing watershed segmentation, level set segmentation and K means clustering. First, the image is preprocessed by removing the skull. Watershed segmentation is applied to this preprocessed image. Level set segmentation is applied to the previous step. Finally, k means clustering is applied as the final step to detect tumor parts accurately. This Hybrid approach is compared with other four techniques such as Threshold segmentation, K means clustering, Watershed segmentation, and Level set-based segmentation methods. Statistical and Visual analysis is performed. It is found that the hybrid approach has better specificity, accuracy, and precision among all four techniques. Further, it is able to detect tumors more accurately. This research could help clinicians in surgical planning, treatment planning and accurately segmenting the tumor part with the most accurate method.
Highlights
Image segmentation is the fundamental step in medical image analysis
A novel hybrid approach is implemented by combing watershed segmentation, level set segmentation and k means clustering
Watershed segmentation is applied to this preprocessed image
Summary
Image segmentation is the fundamental step in medical image analysis. Segmentation is a procedure to separate similar portions of images showing resemblance in different features such as color, intensity, or texture [1]. The basic principle of the watershed segmentation method can be explained by a metaphor-based on the behaviour of water in a landscape. A comparison between Threshold-based, K means clustering-based, Watershed based and Level set-based segmentation methods were performed to detect the tumor region of the brain. Watershed segmentation had the highest accuracy in detecting tumor parts compared to all four techniques. Level set segmentation had the second-highest accuracy among all and was able to detect the exact tumor shape but it took the longest time to display results (3-10 seconds of delay). K means clustering is applied as the final step to detect tumor parts accurately This Hybrid approach is compared with other four techniques such as Threshold segmentation, K means clustering, Watershed segmentation, and Level set-based segmentation methods.
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