Intelligent Segmentation of Medical Images using Fuzzy Bitplane Thresholding

The performance of assessment in medical image segmentation is highly correlated with the extraction of anatomic structures from them, and the major task is how to separate the regions of interests from the background and soft tissues successfully. This paper proposes a fuzzy logic based bitplane method to automatically segment the background of images and to locate the region of interest of medical images. This segmentation algorithm consists of three steps, namely identification, rule firing, and inference. In the first step, we begin by identifying the bitplanes that represent the lungs clearly. For this purpose, the intensity value of a pixel is separated into bitplanes. In the second step, the triple signum function assigns an optimum threshold based on the grayscale values for the anatomical structure present in the medical images. Fuzzy rules are formed based on the available bitplanes to form the membership table and are stored in a knowledge base. Finally, rules are fired to assign final segmentation values through the inference process. The proposed new metrics are used to measure the accuracy of the segmentation method. From the analysis, it is observed that the proposed metrics are more suitable for the estimation of segmentation accuracy. The results obtained from this work show that the proposed method performs segmentation effectively for the different classes of medical images

Patch-based segmentation with spatial context for medical image analysis

Accurate segmentations in medical imaging form a crucial role in many applications from pa- tient diagnosis to population studies. As the amount of data generated from medical images increases, the ability to perform this task without human intervention becomes ever more de- sirable. One approach, known broadly as atlas-based segmentation, is to propagate labels from images which have already been manually labelled by clinical experts. Methods using this ap- proach have been shown to be e ective in many applications, demonstrating great potential for automatic labelling of large datasets. However, these methods usually require the use of image registration and are dependent on the outcome of the registration. Any registrations errors that occur are also propagated to the segmentation process and are likely to have an adverse e ect on segmentation accuracy. Recently, patch-based methods have been shown to allow a relaxation of the required image alignment, whilst achieving similar results. In general, these methods label each voxel of a target image by comparing the image patch centred on the voxel with neighbouring patches from an atlas library and assigning the most likely label according to the closest matches. The main contributions of this thesis focuses around this approach in providing accurate segmentation results whilst minimising the dependency on registration quality. In particular, this thesis proposes a novel kNN patch-based segmentation framework, which utilises both intensity and spatial information, and explore the use of spatial context in a diverse range of applications. The proposed methods extend the potential for patch-based segmentation to tolerate registration errors by rede ning the \locality" for patch selection and comparison, whilst also allowing similar looking patches from di erent anatomical structures to be di erentiated. The methods are evaluated on a wide variety of image datasets, ranging from the brain to the knees, demonstrating its potential with results which are competitive to state-of-the-art techniques.Open Acces