Automated segmentation of medical images is a challenging problem. The number of segments in a medical image may
be unknown a priori, due to the presence or absence of pathological anomalies. Some unsupervised learning techniques
founded in information theory concepts may provide a solid approach to this problem’s solution. We have developed the
Improved “Jump” Method (IJM), a technique that efficiently finds a suitable number of clusters representing different
tissue characteristics in a medical image. IJM works by optimizes an objective function that quantifies the quality of
particular cluster configurations. Recent developments involving interesting relationships between Spectral Clustering
(SC) and kernel Principal Component Analysis (kPCA) are used to extend IJM to the non-linear domain. This novel SC
approach maps the data to a new space where the points belonging to the same cluster are collinear if the parameters of a
Radial Basis Function (RBF) kernel are adequately selected. After projecting these points onto the unit sphere, IJM
measures the quality of different cluster configurations, yielding an algorithm that simultaneously selects the number of
clusters, and the RBF kernel parameter. Validation of this method is sought via segmentation of MR brain images in a
combination of all major modalities. Such labeled MRI datasets serve as benchmarks for any segmentation algorithm.
The effectiveness of the nonlinear IJM is demonstrated in the segmentation of uterine cervix color images for early
identification of cervical neoplasia, as an aid to cervical cancer diagnosis. Studies are in progress in segmentation and
detection of multiple sclerosis lesions.
The difficult problem of identifying dominant structures in unknown data sets has been elegantly addressed
recently by a non-parametric information theoretic approach, the "Jump" method. The method employs an
appropriate but fixed power transformation on the distortion-rate, D(R), curve estimated by the popular K-means
algorithm. Although this approach yields good results asymptotically for higher dimensional spaces, in many
practical cases involving lower dimensional spaces, a transformation function with a fixed power may not find the
correct model order. The work presented here develops an objective function to derive a more suitable
transformation function that minimizes classification error in low dimensional data sets. In addition, a number of
carefully chosen K-means seeding methods based upon proper heuristic choices have been used to enhance the
detection sensitivity and to allow a more accurate estimation. The proposed method has been evaluated for a large
variety of datasets and compared with the original Jump method and other well-known order estimation methods
such as Minimum Description Length (MDL), Akaike Information Criteria (AIC), and Consistent Akaike
Information Criteria (CAIC), demonstrating superior overall performance. Comparative results for the Wisconsin
Diagnostic Breast Cancer Dataset have been included. This modified information theoretic approach to model order
estimation is expected to improve and validate diagnostic classification and detection of pre-cancerous lesions.
Other applications such as finding plausible number of segments in image segmentation scenarios are also possible.a
Efficient retrieval of high quality Regions-Of-Interest (ROI) from high resolution medical images is essential for reliable interpretation and accurate diagnosis. Random access to high quality ROI from codestreams is becoming an essential feature in many still image compression applications, particularly in viewing diseased areas from large medical images. This feature is easier to implement in block based codecs because of the inherent spatial independency of the code blocks. This independency implies that the decoding order of the blocks is unimportant as long as the position for each is properly identified. In contrast, wavelet-tree based codecs naturally use some interdependency that exploits the decaying spectrum model of the wavelet coefficients. Thus one must keep track of the decoding order from level to level with such codecs. We have developed an innovative multi-rate image subband coding scheme using "Backward Coding of Wavelet Trees (BCWT)" which is fast, memory efficient, and resolution scalable. It offers far less complexity than many other existing codecs including both, wavelet-tree, and block based algorithms. The ROI feature in BCWT is implemented through a transcoder stage that generates a new BCWT codestream containing only the information associated with the user-defined ROI. This paper presents an efficient technique that locates a particular ROI within the BCWT coded domain, and decodes it back to the spatial domain. This technique allows better access and proper identification of pathologies in high resolution images since only a small fraction of the codestream is required to be transmitted and analyzed.
Early detection of structural damage to the optic nerve head (ONH) is critical in diagnosis of glaucoma, because such glaucomatous damage precedes clinically identifiable visual loss. Early detection of glaucoma can prevent progression of the disease and consequent loss of vision. Traditional early detection techniques involve observing changes in the ONH through an ophthalmoscope. Stereo fundus photography is also routinely used to detect subtle changes in the ONH. However, clinical evaluation of stereo fundus photographs suffers from inter- and intra-subject variability. Even the Heidelberg Retina Tomograph (HRT) has not been found to be sufficiently sensitive for early detection. A semi-automated algorithm for quantitative representation of the optic disc and cup contours by computing accumulated disparities in the disc and cup regions from stereo fundus image pairs has already been developed using advanced digital image analysis methodologies. A 3-D visualization of the disc and cup is achieved assuming camera geometry. High correlation among computer-generated and manually segmented cup to disc ratios in a longitudinal study involving 159 stereo fundus image pairs has already been demonstrated. However, clinical usefulness of the proposed technique can only be tested by a fully automated algorithm. In this paper, we present a fully automated algorithm for segmentation of optic cup and disc contours from corresponding stereo disparity information. Because this technique does not involve human intervention, it eliminates subjective variability encountered in currently used clinical methods and provides ophthalmologists with a cost-effective and quantitative method for detection of ONH structural damage for early detection of glaucoma.
Feature extraction is a critical preprocessing step, which influences the outcome of the entire process of developing significant metrics for medical image evaluation. The purpose of this paper is firstly to compare the effect of an optimized statistical feature extraction methodology to a well designed combination of point operations for feature extraction at the preprocessing stage of retinal images for developing useful diagnostic metrics for retinal diseases such as glaucoma and diabetic retinopathy. Segmentation of the extracted features allow us to investigate the effect of occlusion induced by these features on generating stereo disparity mapping and 3-D visualization of the optic cup/disc. Segmentation of blood vessels in the retina also has significant application in generating precise vessel diameter metrics in vascular diseases such as hypertension and diabetic retinopathy for monitoring progression of retinal diseases.
The fusion of multi-modal medical images provides a new diagnostic tool with clinical applications. Over the years, image fusion has been used in a number of medical disciplines. However, little fusion work in ophthalmic imaging appears in the literature. With the advent of multi-modal digital information of the retina and advanced image registration programs, the possibility of displaying complementary information in one fused retinal image becomes visually and clinically exciting. The objective of this research was to demonstrate that through fusion of multi-modal retinal information one could increase the information content of retinal pathologies on a fused image. Two aspects of image fusion were addressed in this study: image registration and image fusion of two distinctly different modalities, Fluorescein Angiography (FA) videos and standard color photography. Quantitative analysis of the fusion results was performed using entropy and image noise index. Qualitative analysis was performed by simultaneous visual comparison of two modalities (FA and color) of all registered unfused modes and the fused modes.
This paper describes an automated 3-D surface recovery algorithm for consistent and quantitative evaluation of the deformation in the ONH (optic nerve head). Additional measures, such as the changes in the volume of the cup and the disc as an improvement to the traditional cup to disc ratios, can thus be developed for longitudinal follow-up study of a patient. We propose an automated computerized technique for stereo pair registration and surface visualization of the ONH. Power cepstrum and zero mean cross correlation are embedded in the registration and a 3-D surface recovery technique is proposed. Preprocessing, as well as an overall registration, is performed upon stereo pairs. Then a coarse to fine feature matching strategy is used to reduce the ambiguity in finding the conjugate pair of the same point within the constraints of the epipolar plane. A cubic B-spline interpolation smooths the representation of the ONH obtained, while superimposition of features such as blood vessels is added. Studies show high correlation between traditional cup/disc measures derived from manual segmentation by ophthalmologists and computer generated cup/disc volume ratio. Such longitudinal studies over a large population of glaucoma patients are currently in progress for validation of the surface recovery algorithm.
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