Automatic detection and quantification of brain midline shift using anatomical marker model.

Brain midline shift (MLS) is a significant factor in brain CT diagnosis. In this paper, we present a new method of automatically detecting and quantifying brain midline shift in traumatic injury brain CT images. The proposed method automatically picks out the CT slice on which midline shift can be observed most clearly and uses automatically detected anatomical markers to delineate the deformed midline and quantify the shift. For each anatomical marker, the detector generates five candidate points. Then the best candidate for each marker is selected based on the statistical distribution of features characterizing the spatial relationships among the markers. Experiments show that the proposed method outperforms previous methods, especially in the cases of large intra-cerebral hemorrhage and missing ventricles. A brain CT retrieval system is also developed based on the brain midline shift quantification results.

Unsupervised medical image classification by combining case-based classifiers.

We introduce an automated pathology classification system for medical volumetric brain image slices. Existing work often relies on handcrafted features extracted from automatic image segmentation. This is not only a challenging and time-consuming process, but it may also limit the adaptability and robustness of the system. We propose a novel approach to combine sparse Gabor-feature based classifiers in an ensemble classification framework. The unsupervised nature of this non-parametric technique can significantly reduce the time and effort for system calibration. In particular, classification of medical images in this framework does not rely on segmentation, nor semantic-based or annotation-based feature selection. Our experiments show very promising results in classifying computer tomography image slices into pathological classes for traumatic brain injury patients.

Robust document image binarization technique for degraded document images.

Segmentation of text from badly degraded document images is a very challenging task due to the high inter/intra-variation between the document background and the foreground text of different document images. In this paper, we propose a novel document image binarization technique that addresses these issues by using adaptive image contrast. The adaptive image contrast is a combination of the local image contrast and the local image gradient that is tolerant to text and background variation caused by different types of document degradations. In the proposed technique, an adaptive contrast map is first constructed for an input degraded document image. The contrast map is then binarized and combined with Canny's edge map to identify the text stroke edge pixels. The document text is further segmented by a local threshold that is estimated based on the intensities of detected text stroke edge pixels within a local window. The proposed method is simple, robust, and involves minimum parameter tuning. It has been tested on three public datasets that are used in the recent document image binarization contest (DIBCO) 2009 & 2011 and handwritten-DIBCO 2010 and achieves accuracies of 93.5%, 87.8%, and 92.03%, respectively, that are significantly higher than or close to that of the best-performing methods reported in the three contests. Experiments on the Bickley diary dataset that consists of several challenging bad quality document images also show the superior performance of our proposed method, compared with other techniques.