Wavelet based segmentation of hyperspectral colon tissue imagery

Segmentation is an early stage for the automated classification of tissue cells between normal and malignant types. We present an algorithm for unsupervised segmentation of images of hyperspectral human colon tissue cells into their constituent parts by exploiting the spatial relationship between these constituent parts. This is done by employing a modification of the conventional wavelet based texture analysis, on the projection of hyperspectral image data in the first principal component direction. Results show that our algorithm is comparable to other more computationally intensive methods which exploit the spectral characteristics of the hyperspectral imagery data

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

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

N=1 Supersymmetric Non-Abelian Compensator Mechanism for Extra Vector Multiplet

We present a variant formulation of N=1 supersymmetric compensator mechanism for an arbitrary non-Abelian group in four dimensions. This formulation resembles our previous variant supersymmetric compensator mechanism in 4D. Our field content consists of the three multiplets: (i) A Non-Abelian Yang-Mills multiplet (A_\mu^I, \lambda^I, C_{\mu\nu\rho}^I), (ii) a tensor multiplet (B_{\mu\nu}^I, \chi^I, \varphi^I) and an extra vector multiplet (K_\mu^I, \rho^I, C_{\mu\nu\rho}^I) with the index I for the adjoint representation of a non-Abelian gauge group. The C_{\mu\nu\rho}^I is originally an auxiliary field dual to the conventional auxiliary field D^I for the extra vector multiplet. The vector K_\mu^I and the tensor C_{\mu\nu\rho}^I get massive, after absorbing respectively the scalar \varphi^I and the tensor B_{\mu\nu}^I. The superpartner fermion \rho^I acquires a Dirac mass shared with \chi^I. We fix all non-trivial cubic interactions in the total lagrangian, all quadratic terms in supersymmetry transformations, and all quadratic interactions in field equations. The action invariance and the super-covariance of all field equations are confirmed up to the corresponding orders.Comment: 11 pages, no figure