Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
4 results
Search Results
Item Fully automatic ROI extraction and edge-based segmentation of radius and ulna bones from hand radiographs(PWN-Polish Scientific Publishers bbe@ibib.waw.pl, 2017) Simu, S.; Lal, S.; Nagarsekar, P.; Naik, A.Bone age is a reliable measure of person's growth and maturation of skeleton. The difference between chronological age and bone age indicates presence of endocrinological problems. The automated bone age assessment system (ABAA) based on Tanner and Whitehouse method (TW3) requires monitoring the growth of radius, ulna and short bones (phalanges) of left hand. In this paper, a detailed analysis of two bones in the bone age assessment system namely, radius and ulna is presented. We propose an automatic extraction method for the region of interest (ROI) of radius and ulna bones from a left hand radiograph (RUROI). We also propose an improved edge-based segmentation technique for those bones. Quantitative and qualitative results of the proposed segmentation technique are evaluated and compared with other state-of-the-art segmentation techniques. Medical experts have also validated the qualitative results of proposed segmentation technique. Experimental results reveal that these proposed techniques provide better segmentation accuracy as compared to the other state-of-the-art segmentation techniques. © 2017 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of SciencesItem A study about evolutionary and non-evolutionary segmentation techniques on hand radiographs for bone age assessment(Elsevier Ltd, 2017) Simu, S.; Lal, S.In this paper, a study and performance comparison of various evolutionary and non-evolutionary segmentation techniques on digital hand radiographs for bone age assessment is presented. The segmented hand bones are of vital importance in process of automated bone age assessment (ABAA). Bone age assessment is a technique of checking the skeletal development and detecting growth disorder in a person. However, it is very difficult to segment out the bone from the soft tissue. The problem arises from overlapping pixel intensities between bone region and soft tissue region and also between soft tissue region and background. Thus there is a requirement for a robust segmentation technique for hand bone segmentation. Taking this into consideration we make a comparison between non-evolutionary and evolutionary segmentation algorithms implemented on hand radiographs to recognize bone borders and shapes. The simulation and experimental results demonstrate that multiplicative intrinsic component optimization (MICO) algorithm provides better results as compared to other existing evolutionary and non-evolutionary algorithms. © 2016 Elsevier LtdItem Multi-Res-Attention UNet: A CNN Model for the Segmentation of Focal Cortical Dysplasia Lesions from Magnetic Resonance Images(Institute of Electrical and Electronics Engineers Inc., 2021) Thomas, E.; Pawan, S.J.; Kumar, S.; Horo, A.; Niyas, S.; Vinayagamani, S.; Kesavadas, C.; Rajan, J.In this work, we have focused on the segmentation of Focal Cortical Dysplasia (FCD) regions from MRI images. FCD is a congenital malformation of brain development that is considered as the most common causative of intractable epilepsy in adults and children. To our knowledge, the latest work concerning the automatic segmentation of FCD was proposed using a fully convolutional neural network (FCN) model based on UNet. While there is no doubt that the model outperformed conventional image processing techniques by a considerable margin, it suffers from several pitfalls. First, it does not account for the large semantic gap of feature maps passed from the encoder to the decoder layer through the long skip connections. Second, it fails to leverage the salient features that represent complex FCD lesions and suppress most of the irrelevant features in the input sample. We propose Multi-Res-Attention UNet; a novel hybrid skip connection-based FCN architecture that addresses these drawbacks. Moreover, we have trained it from scratch for the detection of FCD from 3 T MRI 3D FLAIR images and conducted 5-fold cross-validation to evaluate the model. FCD detection rate (Recall) of 92% was achieved for patient wise analysis. © 2013 IEEE.Item Trends in Selected Birth Defects among Parents from Below Poverty Line Population in Karnataka during 2010-2020(Wolters Kluwer Medknow Publications, 2022) Nanjunda, D.C.; Jyothi Lakshmi, S.; Rajesh Acharya, R.H.; Mishra, A.K.The aim of the study is to reveal the common birth defects among parents of newborns belonging to the below poverty line (BPL) category in Karnataka state (South India) by analyzing Suvarna Arogya Suraksha Trust data. In the last 10 years, 3672 kids in BPL families have been born with various birth abnormalities. It is found that 50.3% of newborns have anorectal malformations, 33.1% have hypospadias, 6.0% have diaphragmatic hernia, 5.1% have esophageal atresia, and 2.8% have intestinal atresia and obstruct. As a parent's age rises, the likelihood of having a child with birth abnormalities raise as well, particularly anorectal malformations than diaphragmatic hernia. Male newborns have a higher risk of birth defects. We hypothesized that poverty, material deprivation, and low socioeconomic profile throughout the life course among the BPL community could be some of the key reasons for poor maternal health care and related neonatal outcomes. © 2022 Indian Journal of Public Health | Published by Wolters Kluwer-Medknow.