Shape and data-driven texture segmentation using local binary patterns

We propose a shape and data driven texture segmentation method using local binary patterns (LBP) and active contours. In particular, we pass textured images through a new LBP-based filter, which produces non-textured images. In this “filtered” domain each textured region of the original image exhibits a characteristic intensity distribution. In this domain we pose the segmentation problem as an optimization problem in a Bayesian framework. The cost functional contains a data-driven term, as well as a term that brings in information about the shapes of the objects to be segmented. We solve the optimization problem using level set-based active contours. Our experimental results on synthetic and real textures demonstrate the effectiveness of our approach in segmenting challenging textures as well as its robustness to missing data and occlusions

A Local binary patterns and shape priors based texture segmentation method

We propose a shape and data driven texture segmentation method using local binary patterns (LBP) and active contours. In particular, we pass textured images through a new LBP-based filter, which produces non-textured images. In this “filtered” domain each textured region of the original image exhibits a characteristic intensity distribution. In this domain we pose the segmentation problem as an optimization problem in a Bayesian framework. The cost functional contains a data-driven term, as well as a term that brings in information about the shapes of the objects to be segmented. We solve the optimization problem using level set-based active contours. Our experimental results on synthetic and real textures demonstrate the effectiveness of our approach in segmenting challenging textures as well as its robustness to missing data and occlusions

ANALYSIS OF GALL-BLADDER IMAGES BY USING STATIONARY WAVELET TRANSFORM AND DISCRETE WAVELET TRANSFORM

Ayrıt sezimi algoritmaları biyomedikal görüntü analizinde önemli algoritmalardır. Bu çalışmada ayrıt sezimi için, histogram eşleme, ayrık dalgacık dönüşümü (ADD) ve durağan dalgacık dönüşümü (DDD) yöntemleri, safra kesesi ses üstü imgelerinin kalitesini iyileştirmede kullanılmıştır. Ayrıca, ortanca süzgeçleme algoritması, bu tekniklerden sonra görüntü üzerine uygulanmıştır. Sonuçta bu algoritmaların başarımı, görüntü entropi, parçalı t-testi ve CPU zamanı gibi çeşitli başarım ölçütleri kulllanılarak karşılaştırılmıştır. The edge detection algorithms are important in biomedical image analysis. In this work histogram equalization, the discrete wavelet transform and the stationary wavelet transform techniques were used to improve the quality of the gall bladder ultrasonic images for edge detection. Also the median filtering algorithm was used after applying the both techniques. Then the performances of these algorithms were compared by several performance measures such as image entropy, paired t-test, and CPU time

Segmentation of Calculi from Ultrasound Kidney Images by Region Indictor with Contour Segmentation Method

In this proposed Region Indicator with Contour Segmentation (RICS) method, five major steps are followed to select the exact calculi region from the renal calculi images. In the first and second stage, the region indices library and renal calculi region parameters are computed. After that, the image contrast is enhanced by the Histogram Equalization and the most interested pixel values of enhanced image are selected by the k-means clustering. The most interested pixel values are utilized to find the accurate calculi from the renal images. In the final stage, a number of regions are selected based on the contour process. Subsequently, pixel matching and sequence of thresholding process are performed to find the calculi. In addition, the usage of ANFIS in supervised learning has made the technique more efficient than the previous techniques. Here, the utilization of contour reduces the relative error in between the Expert radiologist and the segmented calculi, which are obtained from the proposed algorithm. Thus, the obtained error is minimized that leads to high efficiency. The implementation result shows the effectiveness of the proposed RICS segmentation method in segmenting the renal calculi in terms of sensitivity and specificity. And also, the proposed method improves the calculi area detection accuracy with reduced in computational time

Ultrasound Mid-end Processing on Android Platform

Commercially-available, portable ultrasound devices are increasingly ubiquitous, but they all utilize ad-hoc processing and display hardware. This inevitably drives up the cost of these devices and usually results in poor user-interface. Many doctors now already carry this hardware around in their pocket on a smartphone, so why not use it to drive down medical device costs and provide a familiar user-interface? This thesis seeks implementation of midend algorithms to develop ultrasound imaging system. Firstly, midend system has been simulated on matlab and then later on android platform for smartphone, to replicate present ultrasound imaging. Later our algorithms related to demodulation, compression and image contrast enhancement are validated by porting them onto android. In order to realize an ultrasound imaging system, several engineering aspects need attention.The signal processing algorithms related to midend design of ultrasound system include envelope detection, compression techniques to t dynamic range and image enhancement techniques to obtain good quality image