Hypothesis Validation of Far-Wall Brightness in Carotid-Artery Ultrasound for Feature-Based IMT Measurement Using a Combination of Level-Set Segmentation and Registration

Intima-media thickness (IMT) is now being considered as an indicator of atherosclerosis. Our group has developed several feature-based IMT measurement algorithms such as the Completely Automated Layer EXtraction (CALEX) (which is a class of patented AtheroEdge Systems from Global Biomedical Technologies, Inc., CA, USA). These methods are based on the hypothesis that the highest pixel intensities are in the far wall of the common carotid artery (CCA) or the internal carotid artery (ICA). In this paper, we verify that this hypothesis holds true for B-mode longitudinal ultrasound (US) images of the carotid wall. This patented methodology consists of generating the composite image (the arithmetic sum of images) from the database by first registering the carotid image frames with respect to a nearly straight carotid-artery frame from the same database using: 1) B-spline-based nonrigid registration and 2) affine registration. Prior to registration, we segment the carotid-artery lumen using a level-set-based algorithm followed by morphological image processing. The binary lumen images are registered, and the transformations are applied to the original grayscale CCA images. We evaluated our technique using a database of 200 common carotid images of normal and pathologic carotids. The composite image presented the highest intensity distribution in the far wall of the CCA/ICA, validating our hypothesis. We have also demonstrated the accuracy and improvement in the IMT segmentation result with our CALEX 3.0 system. The CALEX system, when run on newly acquired US images, shows the IMT error of about 30 mu m. Thus, we have shown that the CALEX algorithm is able to exploit the far-wall brightness for accurate IMT measurements

ThyroScreen system: High resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform

Using right equipment and well trained personnel, ultrasound of the neck can detect a large number of non-palpable thyroid nodules. However, this technique often suffers from subjective interpretations and poor accuracy in the differential diagnosis of malignant and benign thyroid lesions. Therefore, we developed an automated identification system based on knowledge representation techniques for characterizing the intra-nodular vascularization of thyroid lesions. Twenty nodules (10 benign and 10 malignant), taken from 3-D high resolution ultrasound (HRUS) images were used for this work. Malignancy was confirmed using fine needle aspiration biopsy and subsequent histological studies. A combination of discrete wavelet transformation (DWT) and texture algorithms were used to extract relevant features from the thyroid images. These features were fed to different configurations of AdaBoost classifier. The performance of these configurations was compared using receiver operating characteristic (ROC) curves. Our results show that the combination of texture features and DWT features presented an accuracy value higher than that reported in the literature. Among the different classifier setups, the perceptron based AdaBoost yielded very good result and the area under the ROC curve was 1 and classification accuracy, sensitivity and specificity were 100%. Finally, we have composed an Integrated Index called thyroid malignancy index (TMI) made up of these DWT and texture features, to facilitate distinguishing and diagnosing benign or malignant nodules using just one index or number. This index would help the clinicians in more quantitative assessment of the thyroid nodules

Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm

Fatty Liver Disease (FLD) is a progressively prevalent disease that is present in about 15 of the world population. Normally benign and reversible if detected at an early stage, FLD, if left undetected and untreated, can progress to an irreversible advanced liver disease, such as fibrosis, cirrhosis, liver cancer and liver failure, which can cause death. Ultrasound (US) is the most widely used modality to detect FLD. However, the accuracy of US-based diagnosis depends on both the training and expertise of the radiologist. US-based Computer Aided Diagnosis (CAD) techniques for FLD detection can improve accuracy, speed and objectiveness of the diagnosis, and thereby, reduce operator dependability. In this paper, we first review the advantages and limitations of different diagnostic methods which are currently available to detect FLD. We then review the state-of-the-art US-based CAD techniques that utilize a range of image texture based features like entropy, Local Binary Pattern (LBP), Haralick textures and run length matrix in several automated decision making algorithms. These classification algorithms are trained using the features extracted from the patient data in order for them to learn the relationship between the features and the end-result (FLD present or absent). Subsequently, features from a new patient are input to these trained classifiers to determine if he/she has FLD. Due to the use of such automated systems, the inter-observer variability and the subjectivity of associated with reading images by radiologists are eliminated, resulting in a more accurate and quick diagnosis for the patient and time and cost savings for both the patient and the hospital. (C) 2014 Elsevier B.V. All rights reserved

An automated technique for carotid far wall classification using grayscale features and wall thickness variability

PurposeTo test a computer-aided diagnostic method for differentiating symptomatic from asymptomatic carotid B-mode ultrasonographic images. MethodsOur system (called Atheromatic) automatically computed the intima-media thickness (IMT) of the carotid far wall using AtheroEdge, calculated nonlinear features based on higher order spectra, and used these features and IMT and IMT variability (IMTVpoly) to associate each image to a feature vector that was then labeled as symptomatic or asymptomatic (Sym/Asym) by a multiclassifiers system. We tested this method on a database of 118 carotid artery images from 37 symptomatic and 22 asymptomatic patients ResultsThe highest accuracy (99.1) was obtained by the support vector machine classifier using seven features. These features, relevant to discriminate Sym/Asym, included IMT and IMTVpoly, along with the bispectral entropies of the distal wall image at 77 degrees, 78 degrees, and 79 degrees angles. ConclusionsClassification in Sym/Asym of the far carotid wall is feasible and accurate and could be useful for the early detection of atherosclerosis and to identify patients with higher cardiovascular risk. (c) 2014 Wiley Periodicals, Inc. J Clin Ultrasound 43:302-311, 201

Ovarian Tissue Characterization in Ultrasound: A Review

Ovarian cancer is the most common cause of death among gynecological malignancies. We discuss different types of clinical and nonclinical features that are used to study and analyze the differences between benign and malignant ovarian tumors. Computer aided diagnostic (CAD) systems of high accuracy are being developed as an initial test for ovarian tumor classification instead of biopsy, which is the current gold standard diagnostic test. We also discuss different aspects of developing a reliable CAD system for the automated classification of ovarian cancer into benign and malignant types. A brief description of the commonly used classifiers in ultrasound-based CAD systems is also give

Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound

Quantitative characterization of carotid atherosclerosis and classification into symptomatic or asymptomatic type is crucial in both diagnosis and treatment planning. This paper describes a computer-aided diagnosis (CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic based on the textural features. The proposed CAD system consists of three modules. The first module is preprocessing, which conditions the images for the subsequent feature extraction. The feature extraction stage uses image texture analysis to calculate Standard deviation, Entropy, Symmetry, and Run Percentage. Finally, classification is performed using AdaBoost and Support Vector Machine for automated decision making. For Adaboost, we compared the performance of five distinct configurations (Least Squares, Maximum- Likelihood, Normal Density Discriminant Function, Pocket, and Stumps) of this algorithm. For Support Vector Machine, we compared the performance using five different configurations (linear kernel, polynomial kernel configurations of different orders and radial basis function kernels). SVM with radial basis function kernel for support vector machine presented the best classification result: classification accuracy of 82.4%, sensitivity of 82.9%, and specificity of 82.1%. We feel that texture features coupled with the Support Vector Machine classifier can be used to identify the plaque tissue type. An Integrated Index, called symptomatic asymptomatic carotid index (SACI), is proposed using texture features to discriminate symptomatic and asymptomatic carotid ultrasound images using just one index or number. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screenin

Clinicopathologic Profile and Treatment Outcomes of Colorectal Cancer in Young Adults: A Multicenter Study From India

PURPOSEColorectal cancer (CRC) in young adults is a rising concern in developing countries such as India. This study investigates clinicopathologic profiles, treatment patterns, and outcomes of CRC in young adults, focusing on adolescent and young adult (AYA) CRC in a low- and middle-income country (LMIC).METHODSA retrospective registry study from January 2018 to December 2020 involved 126 young adults (age 40 years and younger) with CRC. Patient demographics, clinical features, tumor characteristics, treatment modalities, and survival outcomes were analyzed after obtaining institutional ethics committees' approval.RESULTSAmong 126 AYA patients, 62.70% had colon cancer and 37.30% had rectal cancer. Most patients (67%) were age 30-39 years, with no significant gender predisposition. Females had higher metastatic burden. Abdominal pain with obstruction features was common. Adenocarcinoma (65%) with signet ring differentiation (26%) suggested aggressive behavior. Limited access to molecular testing hindered mutation identification. Capecitabine-based chemotherapy was favored because of logistical constraints. Adjuvant therapy showed comparable recurrence-free survival in young adults and older patients. For localized colon cancer, the 2-year median progression-free survival was 74%, and for localized rectal cancer, it was 18 months. Palliative therapy resulted in a median overall survival of 33 months (95% CI, 18 to 47). Limited access to targeted agents affected treatment options, with only 27.5% of patients with metastatic disease receiving them. Chemotherapy was generally well tolerated, with hematologic side effect being most common.CONCLUSIONThis collaborative study in an LMIC offers crucial insights into CRC in AYA patients in India. Differences in disease characteristics, treatment patterns, and limited access to targeted agents highlight the need for further research and resource allocation to improve outcomes in this population