Automated quantification of ultrasonic fatty liver texture based on curvelet transform and SVD

Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review

Medical image analysis plays a pivotal role in the evaluation of diseases, including screening, surveillance, diagnosis, and prognosis. Liver is one of the major organs responsible for key functions of metabolism, protein and hormone synthesis, detoxification, and waste excretion. Patients with advanced liver disease and Hepatocellular Carcinoma (HCC) are often asymptomatic in the early stages; however delays in diagnosis and treatment can lead to increased rates of decompensated liver diseases, late-stage HCC, morbidity and mortality. Ultrasound (US) is commonly used imaging modality for diagnosis of chronic liver diseases that includes fibrosis, cirrhosis and portal hypertension. In this paper, we first provide an overview of various diagnostic methods for stages of liver diseases and discuss the role of Computer-Aided Diagnosis (CAD) systems in diagnosing liver diseases. Second, we review the utility of machine learning and deep learning approaches as diagnostic tools. Finally, we present the limitations of existing studies and outline future directions to further improve diagnostic accuracy, as well as reduce cost and subjectivity, while also improving workflow for the clinicians.

CHEMOMETRIC ANALYSIS OF DIFFUSE REFLECTANCE SPECTRAL DATA USING SINGULAR VALUE DECOMPOSITION FOR BLOOD GLUCOSE DETECTION

Diabetes mellitus is a metabolic disorder that affects the production or usage of insulin by the body. Diabetes prevails in the body as a long-term condition which causes several other disorders if left unnoticed. Proper control of Diabetes needs continuous monitoring. The current measurement technique is invasive in nature and requires the withdrawal of blood from the body. Periodic quantification of blood glucose leads to pain and discomfort for the subject. This paper presents a non-invasive glucose measuring system using near-infrared diffuse reflectance spectroscopy (DRS). This work attempts to determine the blood glucose value from the diffuse reflected spectra in the NIR region. The study is executed with the spectral signatures of 33 diabetic subjects collected non-invasively using diffuse reflectance spectrometer from a diabetic centre. Blood glucose level of the same subjects are also recorded using the clinical method. The spectral information is subjected to standard normal variate (SNV) preprocessing method to remove baseline drift and then dimension reduction using singular value decomposition (SVD) is applied to the preprocessed data. The extracted singular values when compared with the clinically measured blood glucose is found to have a proportional relationship. The proposed study using singular value decomposition paves us the way for estimating the blood glucose value non-invasively with the obtained set of clinical blood glucose and the corresponding singular value table as a standard reference set.