A nonlinear functional is considered for segmentation of images containing structural textures. A structural texture pattern in an image is characterized by a certain amplitude spectrum, and segmentation of different patterns is obtained by detecting different regions with different amplitude spectra. A gradient-descent-based algorithm is proposed by deriving equations minimizing the functional. This algorithm, implementing the solutions minimizing the functional, is based on the level set method. An effective method employed in this algorithm is shown to be robust in a noisy environment. Experimental results demonstrate that the proposed method outperforms segmentation obtained by using the simulated annealing algorithm based on Gaussian Markov random fields.
Automated medical image diagnosis using quantitative measurements is extremely helpful for cancer prognosis to reach a high degree of accuracy and thus make reliable decisions. In this paper, six morphological features based on texture analysis were studied in order to categorize normal and cancer colon mucosa. They were derived after a series of pre-processing steps to generate a set of different shape measurements. Based on the shape and the size, six features known as Euler Number, Equivalent Diamater, Solidity, Extent, Elongation, and Shape Factor AR were extracted. Mathematical morphology is used firstly to remove background noise from segmented images and then to obtain different morphological measures to describe shape, size, and texture of colon glands. The automated system proposed is tested to classifying 102 microscopic samples of colorectal tissues, which consist of 44 normal color mucosa and 58 cancerous. The results were first statistically evaluated, using one-way ANOVA method in order to examine the significance of each feature extracted. Then significant features are selected in order to classify the dataset into two categories. Finally, using two discrimination methods; linear method and k-means clustering, important classification factors were estimated. In brief, this study demonstrates that abnormalities in low-level power tissue morphology can be distinguished using quantitative image analysis. This investigation shows the potential of an automated vision system in histopathology. Furthermore, it has the advantage of being objective, and more importantly a valuable diagnostic decision support tool.
Multiuser detection constitutes a class of advanced interference mitigation techniques for increasing the capacity of CDMA communication systems. Thus far, the work has been carried out under the Gaussian noise assumption for analytical convenience and yet physical noise encountered in real-life channels is impulsive and decidedly non-Gaussian. Since Gaussian signal processing schemes can perform poorly in impulsive noise, the applicability and performance of such multiuser detectors in realistic channels become strongly questionable.
This paper addresses the development of non-Gaussian techniques for CDMA communications, by first examining the performance degradation of Linear Gaussian-based multiuser detectors in impulsive noise and then by presenting a series of nonlinear techniques to yield a more robust performance. A common approach to linear adaptive interference suppression in Direct Sequence CDMA is based on the Least Mean Square (LMS) or Recursive Least Square (RLS) algorithms to capture the cyclo-stationarity of multiple access interference (MAI) adaptively, mostly under the minimum mean squared error (MMSE) criterion. However, under impulsive noise environments, the performance of the conventional RLS algorithm deteriorates substantially, and therefore, a robust algorithm based on nonlinear RLS is suggested to obtain a modified CDMA receiver structure.
Simulation results are presented to demonstrate that the proposed modified nonlinear RLS algorithm significantly outperforms the conventional RLS algorithm whilst it maintains comparable performance in Gaussian channels.
Accurate and reliable decision making in cancer prognosis can help in the planning of appropriate surgery and therapy and, in general, optimize patient management through the different stages of the disease. In this paper, we present a novel fractal geometry algorithm as a potential method for classifying colorectal histopathological images. 102 microscopic samples of colon tissue were examined in order to identify abnormalities using a morphogical feature approach based on segmenting the image into different classes, derived from fractal dimension. The obtained mean fractal dimension (FD) for normal object tissue was 1.797+/- 0.0381 (n = 44) compared with 1.866+/-0.0262 for malignant samples (n = 58). In brief, this study was able to demonstrate the value of fractal dimension based on morphological approach in the analysis of microscopic colon cancer images. Although, the obtained results are strongly significant in the separation between normal and malignant colorectal images, further analyses are essential to incorporate this methodology into routine clinical practice by supporting pathologist decision.