Qualitative and quantitative comparisons of haemorrhage intracranial segmentation in CT brain images

This paper presents qualitative and quantitative comparisons of our proposed Multi-level Local Segmentation Approach (MLSA) to segment intracranial structures of the CT brain images for haemorrhage detection. The proposed method is able to overcome the main problem in our database images; the inconsistency of grey level values due to different parameter settings during the scanning process that leads to different objects segmented within the same intensity level, as well as helps to automate the segmentation process. One hundred and fifty haemorrhage CT brain images of thirty one patients from Hospital Serdang and Hospital Putrajaya are used in this work. Performance of the segmentation method is quantitatively and qualitatively compared with available automated methods which are watershed and expectation maximization methods. The results show that the MLSA gives the best segmentation of average Percentage of Correct Classification, PCC = 97.1% with 93% of the haemorrhage cases excellently segmented. Besides, qualitatively, it also portrays good segmentation results. The MLSA proves to be accurate and reliable that would provide a strong basis for the application in content-based medical image retrieval.