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A deep learning based approach for classification of abdominal organs using ultrasound images

Santhosh Reddy D., Rajalakshmi P., Mateen M. A.

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 2

779--791

Ultrasound imaging is one of the primary modalities used for diagnosing a multitude of medical conditions affecting organs and soft tissues the body. Unlike X-rays, which use ionizing radiation, ultrasound imaging utilizes non-hazardous acoustic waves and is widely preferred by doctors. However, ultrasound imaging sometimes requires substantial manual effort in the identification of organs during real-time scanning. Also, it is a challenging task if the scanning performed by an unskilled clinician does not comprise adequate information about the organ, leading to an incorrect diagnosis and thereby fatal consequences. Hence, the automated organ classification in such scenarios can offer potential benefits. In this paper, We propose a convolutional neural network-based architecture (CNNs), precisely, a transfer learning approach using ResNet, VGG, GoogleNet, and Inception models for accurate classification of abdominal organs namely kidney, liver, pancreas, spleen, and urinary bladder. The performance of the proposed framework is analyzed using in-house developed dataset comprising of 1906 ultrasound images. Performance analysis shows that the proposed framework achieves a classification accuracy and F1 score of 98.77% and 98.55%, respectively, on an average. Also, we provide the performance of the proposed architecture in comparison with the state-of-the-art studies.

Multi-modal framework for automatic detection of diagnostically important regions in nonalcoholic fatty liver ultrasonic images

Bharath R., Rajalakshmi P., Mateen M. A.

Biocybernetics and Biomedical Engineering

2018

Vol. 38, no. 3

586--601

The severity of fat in ultrasonic liver images is quantified based on characteristics of three regions in the image namely diaphragm, periportal veins and texture of liver parenchyma. The characteristics of these regions vary with the severity of fat in the liver, and is subjected to low signal to noise ratio, low contrast, poorly defined organ boundaries, etc., hence locating these regions in ultrasound images is challenging task for the sonographers. Automated detection of these regions will help the sonographers to do accurate diagnosis in shorter time, and also acts as a fundamental step to develop automated diagnostic algorithms. In this paper, we propose a novel multi-modal framework for detecting diaphragm, periportal veins and texture of liver parenchyma in ultrasonic liver ultrasound images. Since the characteristics of these regions differ from each other, we propose a specific algorithm for detecting each region. Diaphragm and periportal veins are detected with the combination of Viola Jones and GIST descriptor based classifier, while homogeneous texture regions are detected with the combination of histogram features based classifier and connected components algorithm. The proposed algorithm when tested on 180 ultrasound liver images, detected the diaphragm, periportal veins and texture regions with an accuracy of 97%, 91% and 100% respectively.