Wireless capsule endoscopic frame classification scheme based on higher order statistics of multi-scale texture descriptors

The gastrointestinal (GI) tract is a long tube, prone to all kind of lesions. The traditional endoscopic methods do not reach the entire GI tract. Wireless capsule endoscopy is a diagnostic procedure that allows the visualization of the whole GI tract, acquiring video frames, at a rate of two frames per second, while travels through the GI tract, propelled by peristalsis. These frames possess rich information about the condition of the stomach and intestine mucosa, expressed by color and texture in these images. These vital characteristics of each frame can be extracted by color texture analysis. Since texture information is present as middle and high frequency content in the original image, two new images are synthesized from the discrete wavelet coefficients at the lowest and middle scale of a two level Discrete Wavelet Transform of the original frame. These new synthesized images contain essential texture information, at different scales, which can be extracted from statistical descriptors of the coocurrence matrices, which are second-order representations of the synthesized images that encode color and spatial relationships within the pixels of these new images. Since the human perception of texture is complex, a multi-scale and multicolor process based in the analysis of the spatial color variations relationships, is proposed, as classification features. The multicolor texture information is modeled by the third order moments of the texture descriptors sampled at the different color channels. HSV color space is more related to the perceptive human characteristics, therefore it was used in the ambit of this paper. The multi-scale texture information is modeled by covariance of the texture descriptors within the same color channel of the two synthesized images, which contain texture information at different scales. The features are used in a classification scheme using a multilayer perceptron neural network. The proposed method has been applied in real data taken from several capsule endoscopic exams and reaches 94.6% of sensitivity and 93.7% specificity. These results support the feasibility of the proposed algorithm.Center Algoritm

Detection of small bowel tumors in capsule endoscopy frames using texture analysis based on the discrete wavelet transform

Capsule endoscopy is an important tool to diagnosis tumor lesions in the small bowel. The capsule endoscopic images possess vital information expressed by color and texture. This paper presents an approach based in the textural analysis of the different color channels, using the wavelet transform to select the bands with the most significant texture information. A new image is then synthesized from the selected wavelet bands, trough the inverse wavelet transform. The features of each image are based on second-order textural information, and they are used in a classification scheme using a multilayer perceptron neural network. The proposed methodology has been applied in real data taken from capsule endoscopic exams and reached 98.7% sensibility and 96.6% specificity. These results support the feasibility of the proposed algorithm.Centre Algoritm

Detection of small bowel tumors in endoscopic capsule images by modeling non-gaussianity of texture descriptors

This paper presents an approach to the automatic detection of small bowel tumors by processing endoscopic capsule images. The most significant texture information is selected by using wavelet processing and captured in the image domain from an appropriate synthesized image. Co-occurrence matrices are used to derive texture descriptors by modeling second order statistics of color image levels. These descriptors are then modeled by using third and fourth order moments in order to cope with distributions that tend to become non-Gaussian especially in some pathological cases. The proposed approach is supported by a classifier based on radial basis functions procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data and shows that higher order moments can be effective in modeling capsule endoscopic images regarding tumor detection.Centre Algoritm

Automatic detection of small bowel tumors in capsule endoscopy based on color curvelet covariance statistical texture descriptors

Traditional endoscopic methods do not allow the visualization of the entire Gastrointestinal (GI) tract. Wireless Capsule Endoscopy (CE) is a diagnostic procedure that overcomes this limitation of the traditional endoscopic methods. The CE video frames possess rich information about the condition of the stomach and intestine mucosa, encoded as color and texture patterns. It is known for a long time that human perception of texture is based in a multi-scale analysis of patterns, which can be modeled by multi-resolution approaches. Furthermore, modeling the covariance of textural descriptors has been successfully used in classification of colonoscopy videos. Therefore, in the present paper it is proposed a frame classification scheme based on statistical textural descriptors taken from the Discrete Curvelet Transform (DCT) domain, a recent multi-resolution mathematical tool. The DCT is based on an anisotropic notion of scale and high directional sensitivity in multiple directions, being therefore suited to characterization of complex patterns as texture. The covariance of texture descriptors taken at a given detail level, in different angles, is used as classification feature, in a scheme designated as Color Curvelet Covariance. The classification step is performed by a multilayer perceptron neural network. The proposed method has been applied in real data taken from several capsule endoscopic exams and reaches 97.2% of sensitivity and 97.4% specificity. These promising results support the feasibility of the proposed method.Centre Algoritm

Small bowel tumors detection in capsule endoscopy by gaussian modeling of color curvelet covariance coefficients

This paper is concerned with the classification of tumoral tissue in the small bowel by using capsule endoscopic images. The followed approach is based on texture classification. Texture descriptors are derived from selected scales of the Discrete Curvelet Transform (DCT). The goal is to take advantage of the high directional sensitivity of the DCT (16 directions) when compared with the Discrete Wavelet Transform (DWT) (3 directions). Second order statistics are then computed in the HSV color space and named Color Curvelet Covariance (3C) coefficients. Finally, these coefficients are modeled by a Gaussian Mixture Model (GMM). Sensitivity of 99% and specificity of 95.19% are obtained in the testing set.Centre Algoritm

Detecting abnormalities in endoscopic capsule images using color wavelet features and feed-forward neural networks

This paper presents a system to support medical diagnosis and detection of abnormal lesions by processing endoscopic images. Endoscopic images possess rich information expressed by texture. Texture information can be efficiently extracted from medium scales of the wavelet transform. The set of features proposed in this paper to encode textural information is named color wavelet covariance (CWC). CWC coefficients are based on the covariances of second order textural measures, an optimum subset of them is proposed. The proposed approach is supported by a classifier based on multilayer perceptron network for the characterization of the image regions along the video frames. The whole methodology has been applied on real data containing 6 full endoscopic exams and reached 87% specificity and 97.4% sensitivity.Center Algoritm

Kidney targeting and puncturing during percutaneous nephrolithotomy: recent advances and future perspectives

Background and Purpose: Precise needle puncture of the kidney is a challenging and essential step for successful percutaneous nephrolithotomy (PCNL). Many devices and surgical techniques have been developed to easily achieve suitable renal access. This article presents a critical review to address the methodologies and techniques for conducting kidney targeting and the puncture step during PCNL. Based on this study, research paths are also provided for PCNL procedure improvement. METHODS: Most relevant works concerning PCNL puncture were identified by a search of Medline/PubMed, ISI Web of Science, and Scopus databases from 2007 to December 2012. Two authors independently reviewed the studies. RESULTS: A total of 911 abstracts and 346 full-text articles were assessed and discussed; 52 were included in this review as a summary of the main contributions to kidney targeting and puncturing. CONCLUSIONS: Multiple paths and technologic advances have been proposed in the field of urology and minimally invasive surgery to improve PCNL puncture. The most relevant contributions, however, have been provided by the application of medical imaging guidance, new surgical tools, motion tracking systems, robotics, and image processing and computer graphics. Despite the multiple research paths for PCNL puncture guidance, no widely acceptable solution has yet been reached, and it remains an active and challenging research field. Future developments should focus on real-time methods, robust and accurate algorithms, and radiation free imaging techniques.The authors acknowledge Foundation for Science and Technology (FCT) for the fellowships references: SFRH/BPD/46851/2008 and SFRH/BD/74276/2010