Classification of colon biopsy samples by spatial analysis of a single spectral band from its hyperspectral cube

The histopathological analysis of colon biopsy samples is a very important part of screening for colorectal cancer. There is, however, significant inter-observer and even intra-observer variability in the results of such analysis due to its very subjective nature. Therefore, quantitative methods are required for the analysis of histopathological images to aid the histopatholgists in their diagnosis. In this paper, we exploit the shape and structure of the gland nuclei cells for the classification of colon biopsy samples using two-dimensional principal component analysis (2DPCA) and Support Vector Machine (SVM). We conclude that the use of textural features extracted from non-overlapping blocks of the histopathological images results in a non-linear decision boundary which can be efficiently exploited using a SVM with appropriate choice of parameters for its Gaussian kernel. The SVM classifier outperforms all the remaining methods by a clear margin

Model based optimal bit allocation

Modeling of the operational rate-distortion characteristics of a signal can significantly reduce the computational complexity of an optimal bit allocation algorithm. In this report, such models are studied

Vortices and Flat Connections

At Bradlow's limit, the moduli space of Bogomol'nyi vortices on a compact Riemann surface of genus $g$ is determined. The K\"{a}hler form, and the volume of the moduli space is then computed. These results are compared with the corresponding results previously obtained for a general vortex moduli space.Comment: LaTex file, 6 page

Hyperspectral colon tissue cell classification

A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy

Feature detection from echocardiography images using local phase information

Ultrasound images are characterized by their special speckle appearance, low contrast, and low signal-to-noise ratio. It is always challenging to extract important clinical information from these images. An important step before formal analysis is to transform the image to significant features of interest. Intensity based methods do not perform particularly well on ultrasound images. However, it has been previously shown that these images respond well to local phase-based methods which are theoretically intensity-invariant and thus suitable for ultrasound images. We extend the previous local phase-based method to detect features using the local phase computed from monogenic signal which is an isotropic extension of the analytic signal. We apply our method of multiscale feature-asymmetry measurement and local phase-gradient computation to cardiac ultrasound (echocardiography) images for the detection of endocardial, epicardial and myocardial centerline

Stack-run adaptive wavelet image compression

We report on the development of an adaptive wavelet image coder based on stack-run representation of the quantized coefficients. The coder works by selecting an optimal wavelet packet basis for the given image and encoding the quantization indices for significant coefficients and zero runs between coefficients using a 4-ary arithmetic coder. Due to the fact that our coder exploits the redundancies present within individual subbands, its addressing complexity is much lower than that of the wavelet zerotree coding algorithms. Experimental results show coding gains of up to 1:4dB over the benchmark wavelet coding algorithm

The Relevant Operators for the Hubbard Hamiltonian with a magnetic field term

The Hubbard Hamiltonian and its variants/generalizations continue to dominate the theoretical modelling of important problems such as high temperature superconductivity. In this note we identify the set of relevant operators for the Hubbard Hamiltonian with a magnetic field term.Comment: 19 pages, RevTe