Since several lung diseases can be potentially diagnosed based on the patterns of lung tissue observed in medical images, automated texture classification can be useful in assisting the diagnosis. In this paper, we propose a methodology for discriminating between various types of normal and diseased lung tissue in computed tomography (CT) images that utilizes Vector Quantization (VQ), an image compression technique, to extract discriminative texture features. Rather than focusing on images of the entire lung, we direct our attention to the extraction of local descriptors from individual regions of interest (ROIs) as determined by domain experts. After determining the ROIs, we generate "locally optimal" codebooks representing texture features of each region using the Generalized Lloyd Algorithm. We then utilize the codeword usage frequency of each codebook as a discriminative feature vector for the region it represents. We compare k-nearest neighbor, support vector machine and neural network classification approaches using the normalized histogram intersection as a similarity measure. The classification accuracy reached up to 98% for certain experimental settings, indicating that our approach may potentially assist clinicians in the interpretation of lung images and facilitate the investigation of relationships among structure, texture and function or pathology related to several lung diseases.

Probabilistic branching node inference is an important step for analyzing branching patterns involved in many anatomic structures. Based on an approach we have developed previously, we investigate combining machine learning techniques and hybrid image statistics for probabilistic branching node inference, using adaptive boosting as a probabilistic inference framework. Then, we use local image statistics at different image scales for feature representation, including the Harris cornerness, Laplacian, eigenvalues of the Hessian, and Harralick texture features. The proposed approach is applied to a breast imaging dataset consisting of 30 images, 7 of which were previously reported. The use of boosting and the Harralick texture feature further improves upon our previous results, highlighting the role of texture in the analysis of the breast ducts and other branching structures.Index Terms-Branching Structure, Breast Imaging, AdaBoost.

Image segmentation algorithms are critical components of medical image analysis systems. This paper presents a novel and fully automated methodology for segmenting anatomical branching structures in medical images. It is a hybrid approach which integrates the Canny edge detection to obtain a preliminary boundary of the structure and the fuzzy connectedness algorithm to handle efficiently the discontinuities of the returned edge map. To ensure efficient localisation of weak branches, the fuzzy connectedness framework is applied in a sliding window mode and using a voting scheme the optimal connection point is estimated. Finally, the image regions are labelled as tissue or background using a locally adaptive thresholding technique. The proposed methodology is applied and evaluated in segmenting ductal trees visualised in clinical X-ray galactograms and vasculature visualised in angiograms. The experimental results demonstrate the effectiveness of the proposed approach achieving high scores of detection rate and accuracy among state-of-the-art segmentation techniques.

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