Feature extraction for the analysis of colon status from the endoscopic images

BACKGROUND: Extracting features from the colonoscopic images is essential for getting the features, which characterizes the properties of the colon. The features are employed in the computer-assisted diagnosis of colonoscopic images to assist the physician in detecting the colon status. METHODS: Endoscopic images contain rich texture and color information. Novel schemes are developed to extract new texture features from the texture spectra in the chromatic and achromatic domains, and color features for a selected region of interest from each color component histogram of the colonoscopic images. These features are reduced in size using Principal Component Analysis (PCA) and are evaluated using Backpropagation Neural Network (BPNN). RESULTS: Features extracted from endoscopic images were tested to classify the colon status as either normal or abnormal. The classification results obtained show the features' capability for classifying the colon's status. The average classification accuracy, which is using hybrid of the texture and color features with PCA (τ = 1%), is 97.72%. It is higher than the average classification accuracy using only texture (96.96%, τ = 1%) or color (90.52%, τ = 1%) features. CONCLUSION: In conclusion, novel methods for extracting new texture- and color-based features from the colonoscopic images to classify the colon status have been proposed. A new approach using PCA in conjunction with BPNN for evaluating the features has also been proposed. The preliminary test results support the feasibility of the proposed method

PARTER - A PARALLEL SYSTEM FOR TEXTURE RECOGNITION

PARTER is a system for carrying out statistical texture recognition on digital images using parallel processing techniques. It is equipped with a training procedure to build detailed texture models. It uses hierarchical rules and simple statistical classifiers on the information coming from the spatial dependencies of the grey levels, in order to classify each examined region into a texture class. This leads to high discrimination of texture classes and eliminates ‘no decision’ situations during classification. It uses techniques of parallel processing supported by a multitransputer architecture to achieve good time performance. The system has been integrated into an operational unit and tried on different applications with encouraging results concerning accuracy and time. It is currently in use in various texture recognition applications

A comparative study of texture features for the discrimination of gastric polyps in endoscopic video

In this paper, we extend the application of four texture feature extraction methods proposed for the detection of colorectal lesions, into the discrimination of gastric polyps in endoscopic video. Support Vector Machines have been utilized for the texture classification task. The polyp discrimination performance of the surveyed schemes is compared by means of Receiver Operating Characteristics (ROC). The results advocate the feasibility of a computer-based system for polyp detection in video gastroscopy that exploits the textural characteristics of the gastric mucosa in conjunction with its color appearance

An FPGA-based architecture for real time image feature extraction

We propose a novel FPGA-based architecture for the extraction of four texture features using Gray Level Cooccurrence Matrix (GLCM) analysis. These features are angular second moment, correlation, inverse difference moment, and entropy. The proposed architecture consists of a hardware and a software module. The hardware module is implemented on Xilinx Virtex-E V2000 FPGA using VHDL. It calculates many GLCMs and GLCM integer features in parallel. The software retrieves the feature vectors calculated in hardware and performs complementary computations. The architecture was evaluated using standard grayscale images and video clips. The results show that it can be efficiently used in realtime pattern recognition applications

CoLD: a versatile detection system for colorectal lesions in endoscopy video-frames

In this paper, we present CoLD (colorectal lesions detector) an innovative detection system to support colorectal cancer diagnosis and detection of pre-cancerous polyps, by processing endoscopy images or video frame sequences acquired during colonoscopy. It utilizes second-order statistical features that are calculated on the wavelet transformation of each image to discriminate amongst regions of normal or abnormal tissue. An artificial neural network performs the classification of the features. CoLD integrates the feature extraction and classification algorithms under a graphical user interface, which allows both novice and expert users to utilize effectively all system’s functions. It has been developed in close cooperation with gastroenterology specialists and has been tested on various colonoscopy videos. The detection accuracy of the proposed system has been estimated to be more than 95%,,. As it has been resulted, it can be used as a supplementary diagnostic tool for colorectal lesions. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved

Computer-aided thyroid nodule detection in ultrasound images

Nodular thyroid disease is a frequent occurrence in clinical practice and it is associated with increased risk of thyroid cancer and hyperfunction. In this paper we propose a novel method for computer-aided defection of thyroid nodules in ultrasound (US) images. The proposed method is based on a level-set image segmentation approach that takes into account the inhomogeneity of the US images. This novel method was experimentally evaluated using US images acquired from 35 patients. The results show that the proposed method achieves more accurate delineation of the thyroid nodules in the US images and faster convergence than other relevant methods

On different colour spaces for medical colour image classification

Analysis of cells and tissues allow the evaluation and diagnosis of a vast number of diseases. Nowadays this analysis is still performed manually, involving numerous drawbacks, in particular the results accuracy heavily depends on the operator skills. Differently, the automated analysis by computer is performed quickly, requires only one image of the sample and provides precise results. In this work we investigate different texture descriptors extracted from medical images in different colour spaces. We compare these features in order to identify the features set able to properly classify medical images presenting different classification problems. Furthermore, we investigate different colour spaces to identify most suitable for this purpose. The feature sets tested are based on a generalization of some existent grey scale approaches for feature extraction to colour images. The generalization has been applied to the calculation of Grey-Level Co-Occurrence Matrix, Grey-Level Difference Matrix and Grey-Level Run-Length Matrix. Furthermore, we calculate Grey-Level Run-Length Matrix starting from the Grey-Level Difference Matrix. The resulting feature sets performances have been compared using the Support Vector Machine model. To validate our method we have used three different databases, HistologyDS, Pap-smear and Lymphoma, that present different medical problems and so they represent different classification problems. The obtained experimental results have showed that in general features extracted from the HSV colour space perform better than the other and that the best feature subset has been obtained from the generalized Grey-Level Co-Occurrence Matrix, demonstrating excellent performances for this purpose