Online ISSN: 2515-8260

Keywords : Preprocessing


SEGMENTATION OF PANCREATIC CYSTS AND ROI EXTRACTION FROM PANCREATIC CT IMAGES USING MACHINE LEARNING

Mrs. R.Reena Roy; Dr. G.S. Anandha Mala; C. Sarika; S. Shruthi; S. Sripradha

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2981-2991

Segmentation of Pancreas with high accuracy in computerized tomography (CT) results is considered to be a basic issue in both medical image processing and computer-aided diagnosis (CAD). Pancreas segmentation is considered as a difficult task due to its uncertainity in location and in analysis of organs, while it takes very minute division of the entire abdominal CT scans. Because of the accelerated development of the CAD system and therefore the serious need for antiseptic treatments, pancreas segmentation with high accuracy of results is demanded. A new approach is used in this paper, for automated pancreas segmentation of CT images using inter-/intra-slice circumstancial instruction with preprocessing, segmentation, feature extraction, classification.

MAGNETIC RESONANCE MACHINE LEARNING METHOD FOR PREDICTING GEO GRAPHICAL LOCATION SPECIFICATION

Sudhir Sharma; G Shobana; 3L Chandra Sekhar Reddy; P Madhuri; P Naveen

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 3226-3233

Graph theory is a branch of discrete mathematics that deals with the connections among
entities. It has been proven to be a very beneficial and powerful mathematical tool and has a
wide range of applications to handle complex problems in various domains. The aim of this
work is two folds: first, to understand the basic notion of graph theory and second, to
emphasis the significance of graph theory through a real-time application used as a
representational form and characterization of brain connectivity network, as is machine
learning for classifying groups depending on the features extracted from images. This
application uses different techniques including preprocessing, correlations, features or
algorithms. This paper illustrates an automatic tool to perform a standard process using
images of the Magnetic Resonance Imaging (MRI) machine. The process includes preprocessing,
building the graph per subject with different correlations, atlas, relevant feature
extraction according to the literature, and finally providing a set of machine learning
algorithms that can produce analyzable results for physicians or specialists. Further, to
demonstrate the importance of graph theory, this article addresses the most common
applications for graph theory in various fields.