Indiana University-Purdue University Indianapolis (IUPUI) / An algorithm was developed in to efficiently segment the inner-limiting membrane (ILM) and retinal pigmented epithelium (RPE) from spectral domain-optical coherence tomography image volumes. A deformable model framework is described and implemented in which free-form deformations (FFD) are used to direct two deformable sheets to the two high-contrast layers of interest. Improved accuracy in determining retinal thickness (the distance between the ILM and the RPE) is demonstrated against the commercial state-of-the-art Spectralis OCT native segmentation software. A novel adaptation of the highest confidence first (HCF) algorithm is utilized to improve upon the initial results. The proposed adaptation of HCF provides distance-based clique potentials and an efficient solution to layer-based segmentation, reducing a 3D problem to 2D inference.

Medical imagery is increasingly evolving towards higher resolution and throughput. The increasing volume of data and the usage of multiple and often novel imaging modalities necessitates the use of mathematical and computational techniques for quicker, more accurate and more robust analysis of medical imagery. The fields of computer vision and machine learning provide a rich set of techniques that are useful in medical image analysis, in tasks ranging from segmentation to classification and population analysis, notably by integrating the qualitative knowledge of experts in anatomy and the pathologies of various disorders and making it applicable to the analysis of medical imagery going forward. The object of the proposed research is exactly to explore various computer vision and machine learning methods with a view to the improved analysis of multiple modalities of brain and cardiac imagery, towards enabling the clinical goals of studying schizophrenia, brain tumors (meningiomas and gliomas in particular) and cardiovascular disorders. In the first project, a framework is proposed for the segmentation of tubular, branched anatomical structures. The framework uses the tubular surface model which yields computational advantages and further incorporates a novel automatic branch detection algorithm. It is successfully applied to the segmentation of neural fiber bundles and blood vessels. In the second project, a novel population analysis framework is built using the shape model proposed as part of the first project. This framework is applied to the analysis of neural fiber bundles towards the detection and understanding of schizophrenia. In the third and final project, the use of mass spectrometry imaging for the analysis of brain tumors is motivated on two fronts, towards the offline classification analysis of the data, as well as the end application of intraoperative detection of tumor boundaries. SVMs are applied for the classification of gliomas into one of four subtypes towards application in building appropriate treatment plans, and multiple statistical measures are studied with a view to feature extraction (or biomarker detection). The problem of intraoperative tumor boundary detection is formulated as a detection of local minima of the spatial map of tumor cell concentration which in turn is modeled as a function of the mass spectra, via regression techniques.