Terrain Classification using Multiple Image Features

A wide variety of image processing applications require segmentation and classification ofimages. The problem becomes complex when the images are obtained in an uncontrolledenvironment with a non-uniform illumination. The selection of suitable features is a critical partof an image segmentation and classification process, where the basic objective is to identify theimage regions that are homogeneous but dissimilar to all spatially adjacent regions. This paperproposes an automatic method for the classification of a terrain using image features such asintensity, texture, and edge. The textural features are calculated using statistics of geometricalattributes of connected regions in a sequence of binary images obtained from a texture image.A pixel-wise image segmentation scheme using a multi-resolution pyramid is used to correct thesegmentation process so as to get homogeneous image regions. Localisation of texture boundariesis done using a refined-edge map obtained by convolution, thinning, thresholding, and linking.The individual regions are classified using a database generated from the features extracted fromknown samples of the actual terrain. The algorithm is used to classify airborne images of a terrainobtained from the sensor mounted on an aerial reconnaissance platform and the results arepresented

Effect of Learning Rate on the Recognition of Images

This paper presents a study for the effect of learning rate on an approach for texture classification and detection based on the neural network principle. This neural network consists of three layers, which are input, output, and hidden layers. The back propagation technique is considered. A computer algorithm is deduced and applied. In this work, the synthetic textures are generated. The results are taken for the modern computer of AT 486 type. The mathematical analysis is summarized in order to illustrate the effect of learning rate parameter on the exact discrimination during processing. This effect is studied through applications. The minimum consumed time for the computational time of classification in industry is correlated to correspond only the use of only 2 units in the hidden layer of a neural network for real images instead of 11 units

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

Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys

In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist's visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status. In the present paper, the authors present a fully automated algorithm, called RENFAST (Rapid EvaluatioN of Fibrosis And vesselS Thickness), for the segmentation of kidney blood vessels and fibrosis in histopathological images. The proposed method employs a novel strategy based on deep learning to accurately segment blood vessels, while interstitial fibrosis is assessed using an adaptive stain separation method. The RENFAST algorithm is developed and tested on 350 periodic acid-Schiff (PAS) images for blood vessel segmentation and on 300 Massone's trichrome (TRIC) stained images for the detection of renal fibrosis. In the TEST set, the algorithm exhibits excellent segmentation performance in both blood vessels (accuracy: 0.8936) and fibrosis (accuracy: 0.9227) and outperforms all the compared methods. To the best of our knowledge, the RENFAST algorithm is the first fully automated method capable of detecting both blood vessels and fibrosis in digital histological images. Being very fast (average computational time 2.91 s), this algorithm paves the way for automated, quantitative, and real-time kidney graft assessments

Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys

In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist’s visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status. In the present paper, the authors present a fully automated algorithm, called RENFAST (Rapid EvaluatioN of Fibrosis And vesselS Thickness), for the segmentation of kidney blood vessels and fibrosis in histopathological images. The proposed method employs a novel strategy based on deep learning to accurately segment blood vessels, while interstitial fibrosis is assessed using an adaptive stain separation method. The RENFAST algorithm is developed and tested on 350 periodic acid–Schiff (PAS) images for blood vessel segmentation and on 300 Massone’s trichrome (TRIC) stained images for the detection of renal fibrosis. In the TEST set, the algorithm exhibits excellent segmentation performance in both blood vessels (accuracy: 0.8936) and fibrosis (accuracy: 0.9227) and outperforms all the compared methods. To the best of our knowledge, the RENFAST algorithm is the first fully automated method capable of detecting both blood vessels and fibrosis in digital histological images. Being very fast (average computational time 2.91 s), this algorithm paves the way for automated, quantitative, and real-time kidney graft assessments

Thermal Imaging for Enhancing Inspection Reliability: Detection and Characterization

Reliable performance of an equipment or structure depends on pre-service quality and in- service degradation of the equipment or structure under operating conditions. The role of non-destructive testing (NDT) is to ensure integrity, and in turn, reliability of equipment or structure. Besides, NDT can also monitor in-service degradation and to avoid premature failure of the equipments/structures and prevent accidents as well as save human life. Up to now, NDT has been used in various fields of applications such as the inspection o