Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Geometric transform invariant Brain-MR image analysis for tumor detection(2013) Tom, A.; Jidesh, P.In this work we propose a translational, rotational and scaling invariant scheme for possible detection of tumors in Brain-Magnetic Resonance (MR) images. The method incorporates the features like shape, position and texture to accurately diagnose from the infected images. The geometric transformation invariant nature of the method helps in detecting the tumor in various scales, positions and orientations, at a better rate compared to the state-of-the art methods. The method combines three features (shape, position and texture) to form a feature vector, which is used for detecting the infected parts in the image. In order to improve the accuracy of detection process, we employ a preprocessing step to denoise and enhance the images. The result section details the analysis and results of the proposed method and highlights on the accuracy of the method to properly identify the tumor parts in an MR image. © 2013 IEEE.Item Optic disc segmentation using circular hough transform and curve fitting(Institute of Electrical and Electronics Engineers Inc., 2015) Gopalakrishnan, A.; Almazroa, A.; Raahemifar, K.; Lakshminarayanan, V.We present a technique to segment the optic disc (OD) boundary from a color retinal fundus image. The technique used involves the extraction and removal of blood vessels using a top hat transform and an inpainting process. Then, a circular Hough transform is applied to the detected edges to obtain a coarse boundary of the OD and following which probable points of the optic disc are fed to a curve fitting algorithm which uses a higher order polynomial to draw the final boundary of the optic disc. The optic disc segmentation is a crucial part in estimating the CDR (Cup-to-disc ratio) which can be used as an early indicator of glaucoma and for following the progression/remission of the disease. © 2015 IEEE.Item Weakly Supervised Image Annotation and Segmentation(Institute of Electrical and Electronics Engineers Inc., 2021) Naik, D.; Jaidhar, C.D.The various aspects in the processing of an image include object recognition, object classification, image segmentation, and attribute learning, are closely related to each other. In this paper, we proposed a Bayesian Non-parametric (BN) approach to solve the complex visual tasks using the non-parametric property to regulate the model's constraint. A Chinese Restaurant Process Stacked with Weakly Supervised Markov Random Field (WS-MRF-CRP) is developed, which uses Markov Random Field (MRF) for low-level and Chinese Restaurant Process (CRP) for high-level. The proposed approach learns and incorporates association between various object and attribute classes. The input image is clustered into individual components using the MRF, and then the CRP is used for merging the components and generating the image-attribute association. Experiments performed on the Berkeley Segmentation dataset demonstrated that the proposed model performs better than other existing weakly supervised models. © 2021 IEEE.Item Effect of Different Color Spaces on Deep Image Segmentation(Institute of Electrical and Electronics Engineers Inc., 2021) Sushma, B.; Pulikala, P.Image segmentation is an important application in computer vision, proposed to partition an image into meaningful regions on a specific criterion. In recent days, image segmentation tasks have achieved state of the art performance using deep neural and fully connected networks. The datasets used for the segmentation task mainly consist of image data in RGB color space and the deep segmentation architectures are trained without modifying the color space. In this study, the importance of color space is investigated and the obtained results show that the color space can affect the segmentation performance remarkably. Certain regions of interest in images belonging to a particular domain can be segmented better when represented in a certain form of color space. To explore on this two datasets from medical and satellite imagery are considered. The UNET model is modified to accept images as a combination of color spaces and is trained to segment the colonoscopy images for polyps and satellite images for roads under individual and combination of color spaces. Experiments show that the performance of polyp segmentation is better when a combination of HSV+YCbCr color space is considered. Road segmentation in satellite imagery is better in LAB+HSV color space. © 2021 IEEE.Item Cloud Classification in Sky Images using Deep Neural Networks(Institute of Electrical and Electronics Engineers Inc., 2024) Archit; Kumar, A.; Kashyap, Y.In this research endeavor, a thorough analysis of a 10-minute sky video sequence was conducted. The study commenced by extracting frames at a consistent rate of 30 frames per second, resulting in a dataset of 1200 cloud images. Following frame extraction, color image segmentation was applied to identify distinct color regions within each frame.The primary goal was to estimate the percentage of cloudy pixels within each frame. To achieve this, three pre-trained Convolutional Neural Network (CNN) models namely - VGG16, MobileNetV2, and ResNet50 - were employed for cloud detection and pixel classification. This three-model approach contributed to a comprehensive assessment of cloud cover in the images. Cloudy pixel percentage was calculated using both area-based and pixel count-based approaches, adding depth to the analysis. This holistic approach provided a nuanced perspective on cloud cover dynamics, with the results shedding light on the evolution of cloud cover over the video's duration.The report meticulously outlines the methodology employed, encompassing data preprocessing techniques, feature extraction, and model training parameters. It presents the findings of the classification process, including accuracy and performance metrics, and discusses insights gained from the analysis of results. This paper contributes to the scientific discourse by presenting a comprehensive framework for cloud analysis in video sequences, with practical implications for a range of disciplines. © 2024 IEEE.Item Attention Assisted Patch-Wise CNN for the Segmentation of Fluids from the Retinal Optical Coherence Tomography Images(Springer Science and Business Media Deutschland GmbH, 2024) Anoop, B.N.; Parida, S.; Ajith, B.; Girish, G.N.; Kothari, A.R.; Kavitha, M.S.; Rajan, J.Optical Coherence Tomography (OCT) is an important imaging modality in ophthalmology to visualize the abnormalities present in the retina. One of the major reasons for blindness is the accumulation of fluids in the various layers of the retina called retinal cysts. Accurate estimation of the type of cyst and its volume is important for effective treatment planning. In this paper, we propose attention assisted convolutional neural network-based architecture to detect and quantify three types of retinal cysts namely the intra-retinal cyst, sub-retinal cyst and pigmented epithelial detachment from the OCT images of the human retina. The proposed architecture has an encoder-decoder structure with an attention and a multi-scale module. The qualitative and quantitative performance of the model is evaluated on the publicly available RETOUCH retinal OCT fluid detection challenge data set. The proposed model outperforms the state-of-the-art methods in terms of precision, recall, and dice coefficient. Furthermore, the proposed model is computationally efficient due to its less number of model parameters. © Springer Nature Switzerland AG 2024.Item From Pixels to Prognosis: Exploring from UNet to Segment Anything in Mammogram Image Processing for Tumor Segmentation(Institute of Electrical and Electronics Engineers Inc., 2024) Hithesh, M.R.; Vishwanath, V.K.Breast cancer, is the most prevalent form of cancer among women globally accounting for nearly one in four of all new cancer cases. This high prevalence makes breast cancer the second leading cause of death among women making early detection of breast cancer crucial for reducing mortality rates. Mammography, a widely employed medical imaging technique, emerges as a front-line defense against breast cancer. The research aims to achieve pivotal objectives: Firstly, to examine the Segment Anything Model (SAM), created by MetaAI in 2023, and also discuss the model's use in medical image segmentation. Secondly, the study aims to conduct a comprehensive comparative analysis of SAM's segmentation capabilities with other existing segmentation models for medical images. The unique strength of the research lies in incorporating both mass and calcification mammograms within the diverse Curated Breast Imaging Subset of DDSM (CBIS-DDSM) dataset this acknowledges the varied nature of breast cancer, exposing the segmentation models to a wide range of scenarios. Through augmenting the dataset with vertical, horizontal, and rotational transformations, this enables accurate identification and isolation of Region of Interests (RoIs) in mammograms, for robust model training for real-world breast cancer diagnosis. The study investigates different segmentation models for medical image segmentation, with an emphasis on prompt-based (SAM), Encoder-Decoder (UNet, UNet++, Attention-UNet, SegNet) models. The study extends to evaluating these models based on Key Performance Indicators (KPIs) such as Dice score, Intersection over Union (IoU) and Accuracy. The insights gained from this research have broader implications for the application of more accurate segmentation in medical image analysis, and making a significant contribution to the continuous efforts to improve breast cancer diagnostic methodologies. © 2024 IEEE.Item Crack Density and Length Detection using Machine Learning(Avestia Publishing, 2024) Koushik, M.; Hegde, P.; Rudra, B.This study presents a comprehensive approach for detecting and analyzing microscopic cracks in rock samples using computer vision techniques and machine learning algorithms. The proposed methodology involves image segmentation, crack detection, length, and density prediction, utilizing a combination of image processing techniques and linear regression modeling. Microscopic rock images captured at various temperatures were analyzed to detect and measure cracks accurately. The developed system demonstrated effective crack detection and length measurement capabilities, aided by image segmentation, edge detection, and feature extraction methods. Moreover, the application of linear regression facilitated the prediction of crack parameters, exhibiting a clear relationship between crack characteristics and temperature variations. The findings contribute to a deeper understanding of crack formation mechanisms in rocks under different temperature conditions, offering valuable insights for geological studies and infrastructure integrity assessments. © 2024, Avestia Publishing. All rights reserved.Item Cognitive Chromatic Image Synthesis Using UNET and GAN(Institute of Electrical and Electronics Engineers Inc., 2024) Choubey, D.; Patil, V.; Anand Kumar, M.In the last decade, there has been a lot of interest for image colorization over a wide range of applications, especially in the restoration of old or damaged images. Because there are a lot of options when it comes to assigning color information, this problem is ill-posed by nature and is quite difficult to solve. Researchers have handled this issue in a variety of imaginative ways. More recent developments in automated colorization are focused on images that are repetitive in nature or images that require extensive editing. For instance, in such settings, semantic maps can be used as additional input to offer better control over the generalization of the colorization task with the help of conditional Deep Convolutional Generative Adversarial Networks (DCGANs).Our solution combines the techniques to allow computers to produce vivid visuals in this way. Monochrome or black and white images most of the times differ from the colored images in terms of visual detail and image content and colorizing them by hand is a tedious and often an artistic task. © 2024 IEEE.