Pavan Annangi

A region based active contour method for x-ray lung segmentation using prior shape and low level features

In this work, a level set energy for segmenting the lungs from digital Posterior-Anterior (PA) chest x-ray images is presented. The primary challenge in using active contours for lung segmentation is local minima due to shading effects and presence of strong edges due to the rib cage and clavicle. We have used the availability of good contrast at the lung boundaries to extract a multi-scale set of edge/corner feature points and drive our active contour model using these features. We found these features when supplemented with a simple region based data term and a shape term based on the average lung shape, able to handle the above local minima issues. The algorithm was tested on 1130 clinical images, giving promising results.

Understanding the Mechanisms of Deep Transfer Learning for Medical Images

The ability to automatically learn task specific feature representations has led to a huge success of deep learning methods. When large training data is scarce, such as in medical imaging problems, transfer learning has been very effective. In this paper, we systematically investigate the process of transferring a Convolutional Neural Network, trained on ImageNet images to perform image classification, to kidney detection problem in ultrasound images. We study how the detection performance depends on the extent of transfer. We show that a transferred and tuned CNN can outperform a state-of-the-art feature engineered pipeline and a hybridization of these two techniques achieves 20 % higher performance. We also investigate how the evolution of intermediate response images from our network. Finally, we compare these responses to state-of-the-art image processing filters in order to gain greater insight into how transfer learning is able to effectively manage widely varying imaging regimes.

Automated kidney morphology measurements from ultrasound images using texture and edge analysis

In a typical ultrasound scan, a sonographer measures Kidney morphology to assess renal abnormalities. Kidney morphology can also help to discriminate between chronic and acute kidney failure. The caliper placements and volume measurements are often time consuming and an automated solution will help to improve accuracy, repeatability and throughput. In this work, we developed an automated Kidney morphology measurement solution from long axis Ultrasound scans. Automated kidney segmentation is challenging due to wide variability in kidney shape, size, weak contrast of the kidney boundaries and presence of strong edges like diaphragm, fat layers. To address the challenges and be able to accurately localize and detect kidney regions, we present a two-step algorithm that makes use of edge and texture information in combination with anatomical cues. First, we use an edge analysis technique to localize kidney region by matching the edge map with predefined templates. To accurately estimate the kidney morphology, we use textural information in a machine learning algorithm framework using Haar features and Gradient boosting classifier. We have tested the algorithm on 45 unseen cases and the performance against ground truth is measured by computing Dice overlap, % error in major and minor axis of kidney. The algorithm shows successful performance on 80% cases.

Quality metric for parasternal long axis b-mode echocardiograms

This paper presents a method for automatically estimating the quality of Parasternal Long AXis (PLAX) B-mode echocardiograms. The purpose of the algorithm is to provide live feedback to the user on the quality of the acquired image. The proposed approach uses Generalized Hough Transform to compare the structures derived from the incoming image to a representative atlas, thereby providing a quality metric (PQM). On 133 PLAX images from 35 patients, we show: 1) PQM has high correlation with manual ratings from an expert echocardiographer 2) PQM has high correlation with contrast-to-noise ratio, a traditional indicator of image quality 3) on images with high PQM, error in automatic septal wall thickness measurement is low, and vice versa.

A multiresolution support vector machine based algorithm for pneumoconiosis detection from chest radiographs

We consider the problem of detecting the presence of pneumoconiosis in a patient on the basis of evidence found in chest radiographs. Abnormalities pertaining to pneumoconiosis appear in the form of opacities of various sizes; the profusion of these opacities determines the stage of the disease. We present a multiresolution approach whereby we segment regions of interest (ROIs) from the X-Ray image at two levels - lung field and lung zone. We characterize each of these regions using a set of features and build support vector machine (SVM) classifiers that can predict whether or not the region contains any abnormalities. We combine these ROI-level predictions with a second stage SVM in order to get a prediction for the entire chest. Experimental validation shows that this approach provides good results.

Automated posteriorwall thickness measurement from B-mode ultrasound

In this paper, we present a robust algorithm to segment the posterior wall region and estimate wall thickness from parasternal long axis(PLAX) view cardiac US B-mode images. Posterior wall thickness (PWd), Septal wall thickness (SWTd) and Left ventricular Internal diameter(LVId) are used to detect and measure the extent of Left Ventricular Hypertrophy (LVH). Manual measurements of PWd suffers from large inter and intra observer variability due to weak endocardial boundary intertangled with speckle and poor contrast, movement of the fibrous structures like the chordae,papillary muscles and posterior mitral leaflet. The proposed algorithm seeks to address some of these issues by automating the measurement algorithm. The algorithm initially detects epicardial boundary by pericardium detection and later segments the endocardial boundary by a 1D active contour evolution. We have designed the algorithm on a pilot data set of 42 images and validated on 88 patient data sets.The measurement values are in excellent agreement with expert measurements with error = 2.06mm ± 1.5mm.

An automated bladder volume measurement algorithm by pixel classification using random forests

Residual bladder volume measurement is a very important marker for patients with urinary retention problems. To be able to monitor patients with these conditions at the bedside by nurses or in an out patient setting by general physicians, hand held ultrasound devices will be extremely useful. However to increase the usage of these devices by non traditional users, automated tools that can aid them in the scanning and measurement process will be of great help. In our paper, we have developed a robust segmentation algorithm to automatically measure bladder volume by segmenting bladder contours from sagittal and transverse ultrasound views using a combination of machine learning and active contour algorithms. The algorithm is tested on 50 unseen images and 23 transverse and longitudinal image pairs and the performance is reported.Residual bladder volume measurement is a very important marker for patients with urinary retention problems. To be able to monitor patients with these conditions at the bedside by nurses or in an out patient setting by general physicians, hand held ultrasound devices will be extremely useful. However to increase the usage of these devices by non traditional users, automated tools that can aid them in the scanning and measurement process will be of great help. In our paper, we have developed a robust segmentation algorithm to automatically measure bladder volume by segmenting bladder contours from sagittal and transverse ultrasound views using a combination of machine learning and active contour algorithms. The algorithm is tested on 50 unseen images and 23 transverse and longitudinal image pairs and the performance is reported.

Lung partitioning for x-ray CAD applications

Partitioning the inside region of lung into homogeneous regions becomes a crucial step in any computer-aided diagnosis applications based on chest X-ray. The ribs, air pockets and clavicle occupy major space inside the lung as seen in the chest x-ray PA image. Segmenting the ribs and clavicle to partition the lung into homogeneous regions forms a crucial step in any CAD application to better classify abnormalities. In this paper we present two separate algorithms to segment ribs and the clavicle bone in a completely automated way. The posterior ribs are segmented based on Phase congruency features and the clavicle is segmented using Mean curvature features followed by Radon transform. Both the algorithms work on the premise that the presentation of each of these anatomical structures inside the left and right lung has a specific orientation range within which they are confined to. The search space for both the algorithms is limited to the region inside the lung, which is obtained by an automated lung segmentation algorithm that was previously developed in our group. Both the algorithms were tested on 100 images of normal and patients affected with Pneumoconiosis.

Automatic detection and estimation of biparietal diameter from fetal ultrasonography

Fetal bi-parietal diameter (BPD) is known to provide a reliable estimate of gestational age (GA) of a fetus in the first half of pregnancy. In this paper, we present an automated method to identify and measure BPD from B-mode ultrasound images of fetal head. The method (a) automatically detects and places a region-of-interest on the head based on a prior work in our group (b) utilizes the concept of phase congruency for edge detection and (c) employs a cost function to identify the third ventricle inside the head (d) measures the BPD along the perpendicular bisector of occipital frontal diameter (OFD) from the outer rim of the cranium closer to the transducer to the inner rim of the cranium away from the transducer. The cost function is premised on the distribution of anatomical shape, size and presentation of the third ventricle in images that adhere to clinical guidelines describing the scan plane for BPD measurement. The OFD is assumed to lie along the third ventricle. The algorithm has been tested on 137 images acquired from four different scanners. Based on GA estimates and their bounds specified in Standard Obstetric Tables, the GA predictions from automated measurements are found to be within ±2SD of GA estimates from manual measurements by the operator and a second expert radiologist in 98% of the cases. The method described in this paper can also be adapted to assess the accuracy of the scan plane based on the presence/absence of the third ventricle.