Kidney Detection in 3-D Ultrasound Imagery via Shape-to-Volume Registration Based on Spatially Aligned Neural Network.

This paper introduces a computer-aided kidney shape detection method suitable for volumetric (3D) ultrasound images. Using shape and texture priors, the proposed method automates the process of kidney detection, which is a problem of great importance in computer-assisted trauma diagnosis. This paper introduces a new complex-valued implicit shape model, which represents the multiregional structure of the kidney shape. A spatially aligned neural network classifiers with complex-valued output is designed to classify voxels into background and multiregional structure of the kidney shape. The complex values of the shape model and classification outputs are selected and incorporated in a new similarity metric, such as the shape-to-volume registration process only fits the shape model on the actual kidney shape in input ultrasound volumes. The algorithm's accuracy and sensitivity are evaluated using both simulated and actual 3-D ultrasound images, and it is compared against the performance of the state of the art. The results support the claims about accuracy and robustness of the proposed kidney detection method, and statistical analysis validates its superiority over the state of the art.

An Automated Approach for Kidney Segmentation in Three-Dimensional Ultrasound Images.

Automated segmentation of kidneys in three-dimensional (3-D) abdominal ultrasound volumes is a task of paramount importance in automated diagnosis of abdominal trauma. However, ultrasound speckle noise, low-contrast boundaries, partial kidney occlusion, and probe misalignment restrict the utility of the solution, especially when it is used in emergency rooms and Focused Assessment with Sonography Trauma applications. This paper introduces a systematic and cost-effective method capable of detecting and segmenting the kidney's shape in acquired 3-D ultrasound volumes, using off-line training datasets. This paper offers a new shape model representation, called the complex-valued implicit shape model, to generate a 3-D kidney shape model by combining prior knowledge of training shapes and anatomical knowledge. We apply shape-to-volume registration, based on a new similarity metric, to detect the kidney shape by fitting the 3-D shape model on 3-D ultrasound volumes. Upon kidney detection, the fitted shape model is used to initialize and evolve a new level-set function, called complex-valued rational level-set with shape prior, to segment the kidney's shape. Experimentation using both simulated and actual ultrasound volumes indicate that the proposed solution provides a better performance over the state-of-the-art volumetric ultrasound segmentation methods.

Atlas-based segmentation of abdominal organs in 3D ultrasound, and its application in automated kidney segmentation.

Automated segmentation of abdominal organs in 3D ultrasound images is an important and challenging task toward computer assisted emergency diagnosis. However, speckle noise, low-contrast organ tissues, intensity-profile inhomogeneity, and partial organ visibility are some ultrasound challenges which limits the utility of the automated diagnosis solutions. In this paper, an atlas-based method to automatically segment an organ of interest in abdominal 3D ultrasound images is proposed. The atlas model contains texture information and shape knowledge of the organ, which facilitates an accurate discrimination of organ from non-organ voxels in input 3D ultrasound images. The proposed method offers a mechanism to automatically detect the organ, and therefore, it eliminates the need of manual initialization of organ segmentation. The proposed method is applied to automatically segment the right kidney in 3D ultrasound images. The experimental results indicate that the proposed method provides a higher detection and segmentation accuracy compared to state-of-the-art.

Shape-based kidney detection and segmentation in three-dimensional abdominal ultrasound images.

Due to recent technical advancements of three-dimensional ultrasound imaging systems, applications of this imaging modality have been expanding from the fetal imaging to cardiac- and abdominal-diagnosis. Among all internal organs, diagnosing the kidney has a paramount importance for rapid bedside treatment of trauma and kidney stone patients using ultrasound images. Although three-dimensional ultrasound provides higher level of structural information of kidneys, manual kidney diagnosis using three-dimensional ultrasound images requires a highly trained medical staff, due to the extensive visual complexity which three-dimensional images contain. Therefore, computer aided automated kidney diagnosis becomes very essential. Due to the challenging problems of ultrasound images, such as speckle noise and inhomogeneous intensity profile, kidney segmentation in three-dimensional ultrasound images has not been sufficiently investigated by researchers. In this paper, we first propose a new automated kidney detection approach using three-dimensional Morison's pouch ultrasound images. Then, we proposed a shape-based method to segment the detected kidneys. A preprocessing step is utilized to overcome the ultrasound challenges. Based on a set of 14 ultrasound volumes, we have evaluated the detection rate of our proposed kidney detection approach which is 92.86%. For kidney segmentation, we compared our proposed method with an existing approach, and the performed statistical analysis strongly validates the superiority of our proposed method with p = 0.000032.

Medical image denoising using low pass filtering in sparse domain.

In this work, we introduce a new approach for medical image denoising. An innovative method is proposed to extend the concept of low-pass filtering to the sparse representation framework. A weight matrix is applied to the definition of the sparse coding optimization problem intended to reduce coefficients corresponding to atoms with higher frequency contents, which dominantly represent the image noise. In parallel, a new overcomplete Discrete Cosine Transform (DCT) dictionary is constructed to include both frequency and phase information, aiming to remove blocking artifacts without considering patch-overlap. The proposed denoising approach was applied on low-dose Computed Tomography (CT) phantoms. The resultant observations demonstrate qualitative and quantitative improvements, in terms of peak signal to noise ratio (PSNR), in comparison to some previous approaches.

Active ellipse model and automatic chamber detection in apical views of echocardiography images.

In this article, an automatic method for detection of all chambers in apical two- and four-chamber views is proposed. The method is based on four evolving ellipses with their sizes and alignments (centre point) gradually changing through iterations until they reach to the point that approximates the chamber boundaries. The interaction between the internal, external and inter-elliptic forces controls the simultaneous evolution of ellipses. Since no prior assumption of the approximate location is required with our approach, the specialists are not required to locate the centre points of chambers in apical images, making the overall segmentation fully automated. Moreover, the resultant ellipse inside a chamber could be used as the initial contour in segmentation techniques such as active contour models, where the initial contour has a significant role for higher accuracy and faster convergence. The simplicity of equations developed in our approach make for a computationally faster algorithm, compared with former approaches that utilize morphologic operators. Our evolving ellipse does not go beyond the gaps, a problem that normally exists within boundaries in echo images, making our overall segmentation process more robust against the gaps. To evaluate the proposed method, a subset of 80 images is selected and three observers are requested to manually draw best ellipses inside the images and compare them with our results. The obtained dice coefficient results (87.62 ± 4.53% for observer-1, 83.18 ± 6.20% for observer-2, 86.02 ± 5.16% for observer-3) indicate that the proposed method has a useful performance.

Segmenting echocardiography images using B-Spline snake and active ellipse model.

In this paper, a fully automated method for segmenting Left Ventricle (LV) in echocardiography images is proposed. A new method named active ellipse model is developed to automatically find the best ellipse inside the LV chamber without intervention of any specialist. A modified B-Spline Snake algorithm is used to segment the LV chamber in which the initial contour is formed by the predefined ellipse. As a result of using active ellipse model, the segmentation is extricated from dealing with gaps within myocardium boundary which are highly problematic in echocardiography image segmentation. Based on the results obtained from different studies, the proposed method is faster and more accurate than previous approaches. Our method is evaluated on 20 sets of echocardiography images of patients; and acquired results (92.30 ± 4.45% dice's coefficient) indicate the proposed method has remarkable performance.

Endocardial boundary extraction in left ventricular echocardiographic images using fast and adaptive B-spline snake algorithm.

PURPOSE: A fast and robust algorithm was developed for automatic segmentation of the left ventricular endocardial boundary in echocardiographic images. The method was applied to calculate left ventricular volume and ejection fraction estimation. METHODS: A fast adaptive B-spline snake algorithm that resolves the computational concerns of conventional active contours and avoids computationally expensive optimizations was developed. A combination of external forces, adaptive node insertion, and multiresolution strategy was incorporated in the proposed algorithm. Boundary extraction with area and volume estimation in left ventricular echocardiographic images was implemented using the B-spline snake algorithm. The method was implemented in MATLAB and 50 medical images were used to evaluate the algorithm performance. Experimental validation was done using a database of echocardiographic images that had been manually evaluated by experts. RESULTS: Comparison of methods demonstrates significant improvement over conventional algorithms using the adaptive B-spline technique. Moreover, our method reached a reasonable agreement with the results obtained manually by experts. The accuracy of boundary detection was calculated with Dice's coefficient equation (91.13%), and the average computational time was 1.24 s in a PC implementation. CONCLUSION: In sum, the proposed method achieves satisfactory results with low computational complexity. This algorithm provides a robust and feasible technique for echocardiographic image segmentation. Suggestions for future improvements of the method are provided.

Fast and automatic LV mass calculation from echocardiographic images via B-spline snake model and Markov random fields.

Left ventricular (LV) mass has several important diagnostic and indicative implications. In this paper, a fast and accurate technique for detection of inner and outer boundaries of LV and, consequently, calculation of LV mass from apical 4-chamber echocardiographic images is presented. For detection of the inner boundary, a modified B-spline snake is proposed, which relies merely on image intensity and obviates the need for computationally-demanding image forces. The outer boundary is then obtained using a Markov random fields model in the neighborhood of the estimated inner border. Experimental validation of the proposed technique demonstrates remarkable improvement over conventional algorithms.

Object contour extraction in medical images by fast adaptive B-Snake.

Detection of edge contours to define the object class and segmenting it from the background(s) class is a major challenge in medical image processing. In this paper, we introduce a fast adaptive B-Spline Snake algorithm that overcomes the limitations of previous B-Snakes by avoiding computationally expensive optimization stages. Furthermore, we present novel strategies for adaptive knot insertion to fulfill the specific requirements of the medical application accordingly. Experimental results on pulmonary CT images and brain MR images demonstrated that our method is superior both in accuracy and convergence speed over previous B-Spline Snake algorithms. The proposed algorithm is also robust to missing the boundaries.