Journal Articles
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Item State-of-the-art review on automated lumen and adventitial border delineation and its measurements in carotid ultrasound(Elsevier Ireland Ltd, 2018) Kumar, P.K.; Araki, T.; Rajan, J.; Laird, J.R.; Nicolaïdes, A.; Suri, J.S.Background and objective: Accurate, reliable, efficient, and precise measurements of the lumen geometry of the common carotid artery (CCA) are important for (a) managing the progression/regression of atherosclerotic build-up and (b) the risk of stroke. The image-based degree of stenosis in the carotid artery and the plaque burden can be predicted using the automated carotid lumen diameter (LD)/inter-adventitial diameter (IAD) measurements from B-mode ultrasound images. The objective of this review is to present the state-of-the-art methods and systems for the measurement of LD/IAD in CCA based on automated or semi-automated strategies. Further, the performance of these systems is compared based on various metrics for its measurements. Methods: The automated algorithms proposed for the segmentation of carotid lumen are broadly classified into two different categories as: region-based and boundary-based. These techniques are discussed in detail specifying their pros and cons. Further, we discuss the challenges encountered in the segmentation process along with its quantitative assessment. Lastly, we present stenosis quantification and risk stratification strategies. Results: Even though, we have found more boundary-based approaches compared to region-based approaches in the literature, however, the region-based strategy yield more satisfactory performance. Novel risk stratification strategies are presented. On a patient database containing 203 patients, 9 patients are identified as high risk patients, whereas 27 patients are identified as medium risk patients. Conclusions: We have presented different techniques for the lumen segmentation of the common carotid artery from B-mode ultrasound images and measurement of lumen diameter and inter-adventitial diameter. We believe that the issue regarding boundary-based techniques can be compensated by taking regional statistics embedded with boundary-based information. © 2018 Elsevier B.V.Item Image Analysis of Nuclei Histopathology Using Deep Learning: A Review of Segmentation, Detection, and Classification(Springer, 2023) Kadaskar, M.; Patil, N.Deep learning has recently advanced in its applicability to computer vision challenges, and medical imaging has become the most used technique in histopathology image analysis. Nuclei instance segmentation, detection, and classification are one such task. Reliable analysis of these image slides is critical in cancer identification, treatment, and care. Researchers have recently been interested in this issue. This study reviews the categorization and investigation of strategies utilized in recent works to improve the effectiveness of automated nuclei segmentation, detection, and classification in histopathology images. It critically examines state-of-the-art deep learning techniques, analyzes the trends, identifies the challenges, and highlights and helps with the future directions for research. The taxonomy includes deep learning techniques, enhancement, and optimization methods. The survey findings will help to overcome the challenges of nuclei segmentation, detection, and classification while improving the performance of models and, thus, aid future research plans. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.Item Advanced thermal vision techniques for enhanced fault diagnosis in electrical equipment: a review(Springer, 2025) Anbalagan, A.; Persiya, J.; Mohamed Mansoor Roomi, S.; Arumuga Perumal, D.A.; Poornachari, P.; Vijayalakshmi, M.; Ebenezer, L.Ensuring the reliability and safety of electrical equipment is essential for industrial and residential applications. Traditional fault diagnosis methods involving physical inspections are time-consuming and ineffective for early fault detection. Infrared (IR) thermography offers a non-invasive and efficient solution by identifying anomalies in temperature profiles. This review explores thermal vision-based fault diagnosis techniques, including region of interest (ROI) segmentation, image pre-processing, and fault diagnosis algorithms, with a focus on deep learning approaches. The study highlights the effectiveness of machine learning models in enhancing fault detection accuracy while identifying challenges such as environmental variations, data inconsistencies, and system integration issues. The review discusses the role of real-time applications, wireless technologies, and AI-based automation in improving fault detection. Research gaps are identified, and future directions are proposed to enhance efficiency, reliability, and industrial adoption. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025.Item ANet: Nuclei Instance Segmentation and Classification with Attention-Based Network(Springer, 2024) Kadaskar, M.; Patil, N.The segmentation and classification of nuclei in haematoxylin and eosin-stained images is critical for diagnosing cancer and other disorders. Developing automated methods is necessary for the quantitative analysis of whole-slide images and further downstream analysis. However, many challenges are to be solved, such as varying morphology and observer differences. To address these concerns, we present ANet, an encoder–decoder structure based on attention mechanisms for nuclear segmentation and classification that makes use of information in high-dimensional features improved by attention. These blocks generate meaningful feature activation and eliminate irrelevant information to produce finer maps. It segments the touching, clustered, and overlapping nuclei and classifies them using upsampling branches. Our method includes components such as PreAct-ResNet50, residual attention, convolutional block attention module, and dense attention unit. We demonstrate how our approach achieves cutting-edge performance on several multi-tissue histopathology datasets such as Kumar, CoNSeP, and CPM17. We also demonstrate our model’s generalization capabilities on other combinations of datasets, including CPM15 and TNBC. ANet demonstrates a notable improvement of 1.14%, 2.70%, 1.41%, and 1.29% in Dice, AJI, SQ, and PQ scores, respectively, for the CPM17 dataset. In addition, it achieves a 1.18% improvement in AJI score for the Kumar dataset. Despite the inherent challenges in nuclei classification within the CoNSeP dataset, ANet yields outstanding results, showcasing a substantial improvement of 9.74%, 3.97%, and 0.80% in F1 scores for the inflammatory, spindle, and miscellaneous classes. Furthermore, ANet exhibits strong generalization across the CPM dataset, TNBC, and Combined CoNSeP, with improvements observed in the majority of metrics. The given improvement is justifiable, as are the interpretable visual results. The proposed method is of great potential for analyzing histopathology images, demonstrated by an increment of performance in multiple metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.