Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Effective analysis of breast thermography needs an accurate segmentation of the inflamed region in Infrared Breast Thermal Images (IBTI) which helps in the diagnosis of breast cancer. However, IBTI suffers from intensity inhomogeneity, overlapping regions of interest, poor contrast, and low signal-to-noise ratio (SNR) due to the imperfect image acquisition process. To mitigate this, this work proposes an enhanced segmentation of the inflamed Region of Interest (ROI) using an active contour method driven by the multiscale local and global fitted image (MLGFI) model. The first phase proposes a bilateral histogram difference-based thresholding (BHDT) method for locating the inflamed ROI. This is then used for automatic initialization of active contours driven by MLGFI to segment the inflamed ROI from IBTI effectively. To prove the effectiveness of this segmentation method, its performance is compared with ground truth image and its accuracy is also evaluated with the state-of-the-art methods (Fuzzy C Means (FCM), Chan-Vese (CV-ACM), and K-means). From the analysis, it is found that the proposed method not only increases the precision and the segmentation accuracy but also reduces the oversegmentation and undersegmentation rate significantly. In the second phase, area-based feature (AF) and average intensity-based feature (AIF) along with the GLCM (gray level cooccurrence matrix) based second-order statistical features are extracted from the inflamed ROI. Based on these features, a system is developed to effectively classify the benign and malignant breast conditions. From the results, it is observed that the proposed model exhibits an improved accuracy of 91.5%, sensitivity of 91%, and specificity of 92% compared to the whole breast thermogram. Hence, it is concluded that the proposed method will improve the efficacy of thermal imaging in the diagnosis of breast cancer. © 2021 Nirmala Venkatachalam et al.

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School of Computer Science and Engineering

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Fulltext

Journal of Oncology

Enhanced Segmentation of Inflamed ROI to Improve the Accuracy of Identifying Benign and Malignant Cases in Breast Thermogram

This paper describes an automated diseases detection using remote sensing images. Agriculturists are facing loss due to various crop diseases. It becomes tedious to the cultivators to monitor the crops regularly when the cultivated area is huge (in acres). The most significant part of our research is early detection the disease as soon as it starts spreading on the top layer of the leaves using remote sensing images. This approach has two phases: first phase deals with training of healthy and as well as diseased datasets i.e.) the extraction of threshold values from the image, second phase deals with monitoring of crops and identification of particular disease using canny edge detection algorithm and histogram analysis and also intimate the agriculturists with an early alert message immediately. © 2017 IEEE.

Proceedings - 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development, TIAR 2017

Disease detection in crops using remote sensing images

In the field of dentistry, prospective clinical study reports affirm the need for approximate growth analysis of endodontic tooth post treatment. There is no difference in the frequency, appearance or extent of root resorption in the teeth. It is necessary to elucidate the role of endodontic treatment in the root resorption. Differences between the two samples (radiographs), which are taken with specified period of intervals, in terms of the frequency of growth changes in treated teeth is needed to observe accurately. This study mainly aims at this requirement by utilising slightly modified region-based growing active contour model for quantitative growth analysis of tooth. Recall radiographs of endodontic regeneration involving immature permanent teeth with pulp necrosis have been considered as input in this research. Image enhancing techniques such as dilation and erosion of mathematical morphology are then performed sequentially to emphasise the outlying pixels. Finally, the criterions which include the root length, apical diameter and the dentinal wall thickness are calibrated and given in experimental results. The visual sample results and along with its measurements proved the efficiency of the proposed algorithm. © The Institution of Engineering and Technology.

IET Image Processing

Quantitative growth analysis of pulp necrotic tooth (post-op) using modified region growing active contour model

The highways now offer more and more complex road junctions composed of many surrounding roads, overlapping each other with high curvature. Traditional road detection methods are not adequate for the rapid development of cities with increased complexity of the road junction shape. The major challenges in road extraction are varying spectral reflectance, lane markings, obstacles with different sizes, of various shapes, and intersected roads. However, very few researchers have attempted handling overlapped roads with low curvature only. In this study, a water flow-based semi-automatic approach is proposed for extracting road network with various shapes of junctions (Y-shaped with different acute angles), intersected and also for overlapped high curvilinear roads. Recognising the complex road junction is done with fewer automatically generated anchor points without human intervention, which detects the number of roads (branches) connected to that junction along the road's width, orientation and length with less computation time. Hence, from a manually selected seed point, the authors' algorithm can be automatically propagated throughout a whole road network with or without single lane or multiple lanes (lined, dotted or both). Experimental results show that this proposed approach can accurately and efficiently extract interconnected road network from QuickBird images with minimal seed points. © The Institution of Engineering and Technology.

Junction-aware water flow approach for urban road network extraction

Transport affects agricultural marketing because it is the only means by which farmers can transport their produce to the market. Reliable road path is essential for transporting the cultivated crops to the market at right time. Heavy traffic, railway crossings, road repair and unexpected road blocks increases the transportation time. The increase in transportation time results in loses of crop freshness and quality which reduces the profit for farmers. The impacts of road infrastructure on agricultural output and productivity are particularly important in rural development. In this paper, we propose a methodology to detect reliable road path with reduced transportation time in the presence of road blocks or damages. The road network with the length and width of roads (both damaged as well as good) can be extracted accurately from a given remote sensing image. Discontinuities of roads are identified (due to damage or block) using multilevel searching by iteratively increasing the jump step size in pixels. The experimental results shows that the efficiency and accuracy of the proposed methodology. © 2015 IEEE.

Proceedings - 2015 IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural Development, TIAR 2015

Damaged road detection in rural areas for improving agricultural marketing

A width and color based geometric active deformable model is proposed for road network extraction from remote sensing images with minimal human interception. Orientation and width of road are computed from a single manual seed point, from which the propagation starts both right and left hand directions of the starting point, which extracts the interconnected road network from the aerial or high spatial resolution satellite image automatically. Here the propagation (like water flow in canal with defined boundary) is restricted with color and width of the road. Road extraction is done for linear, curvilinear (U shape and S shape) roads first, irrespective of width and color. Then, this algorithm is improved to extract road with junctions in a shape of L, T and X along with center line. Roads with small break or disconnected roads are also extracts by a modified version of this same algorithm. This methodology is tested and evaluated with various remote sensing images. The experimental results show that the proposed method is efficient and extracting roads accurately with less computation time. However, in complex urban areas, the identification accuracy declines due to the various sizes of obstacles, over bridges, multilane etc. © 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).