Faculty Publications
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Item Multispectral satellite image denoising via adaptive cuckoo search-based wiener filter(Institute of Electrical and Electronics Engineers Inc., 2018) Suresh, S.; Lal, S.; Chen, C.; Çelik, T.Satellite image denoising is essential for enhancing the visual quality of images and for facilitating further image processing and analysis tasks. Designing of self-tunable 2-D finite-impulse response (FIR) filters attracted researchers to explore its usefulness in various domains. Furthermore, 2-D FIR Wiener filters which estimate the desired signal using its statistical parameters became a standard method employed for signal restoration applications. In this paper, we propose a 2-D FIR Wiener filter driven by the adaptive cuckoo search (ACS) algorithm for denoising multispectral satellite images contaminated with the Gaussian noise of different variance levels. The ACS algorithm is proposed to optimize the Wiener weights for obtaining the best possible estimate of the desired uncorrupted image. Quantitative and qualitative comparisons are conducted with 10 recent denoising algorithms prominently used in the remote-sensing domain to substantiate the performance and computational capability of the proposed ACSWF. The tested data set included satellite images procured from various sources, such as Satpalda Geospatial Services, Satellite Imaging Corporation, and National Aeronautics and Space Administration. The stability analysis and study of convergence characteristics are also performed, which revealed the possibility of extending the ACSWF for real-time applications as well. © 1980-2012 IEEE.Item NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images(Elsevier Ltd, 2021) Lal, S.; Das, D.; Alabhya, K.; Kanfade, A.; Kumar, A.; Kini, J.R.The nuclei segmentation of hematoxylin and eosin (H&E) stained histopathology images is an important prerequisite in designing a computer-aided diagnostics (CAD) system for cancer diagnosis and prognosis. Automated nuclei segmentation methods enable the qualitative and quantitative analysis of tens of thousands of nuclei within H&E stained histopathology images. However, a major challenge during nuclei segmentation is the segmentation of variable sized, touching nuclei. To address this challenge, we present NucleiSegNet - a robust deep learning network architecture for the nuclei segmentation of H&E stained liver cancer histopathology images. Our proposed architecture includes three blocks: a robust residual block, a bottleneck block, and an attention decoder block. The robust residual block is a newly proposed block for the efficient extraction of high-level semantic maps. The attention decoder block uses a new attention mechanism for efficient object localization, and it improves the proposed architecture's performance by reducing false positives. When applied to nuclei segmentation tasks, the proposed deep-learning architecture yielded superior results compared to state-of-the-art nuclei segmentation methods. We applied our proposed deep learning architecture for nuclei segmentation to a set of H&E stained histopathology images from two datasets, and our comprehensive results show that our proposed architecture outperforms state-of-the-art methods. As part of this work, we also introduced a new liver dataset (KMC liver dataset) of H&E stained liver cancer histopathology image tiles, containing 80 images with annotated nuclei procured from Kasturba Medical College (KMC), Mangalore, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India. The proposed model's source code is available at https://github.com/shyamfec/NucleiSegNet. © 2020 Elsevier LtdItem Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images(Elsevier Ltd, 2021) Aatresh, A.A.; Yatgiri, R.P.; Chanchal, A.K.; Kumar, A.; Ravi, A.; Das, D.; Raghavendra, B.S.; Lal, S.; Kini, J.Image segmentation remains to be one of the most vital tasks in the area of computer vision and more so in the case of medical image processing. Image segmentation quality is the main metric that is often considered with memory and computation efficiency overlooked, limiting the use of power hungry models for practical use. In this paper, we propose a novel framework (Kidney-SegNet) that combines the effectiveness of an attention based encoder-decoder architecture with atrous spatial pyramid pooling with highly efficient dimension-wise convolutions. The segmentation results of the proposed Kidney-SegNet architecture have been shown to outperform existing state-of-the-art deep learning methods by evaluating them on two publicly available kidney and TNBC breast H&E stained histopathology image datasets. Further, our simulation experiments also reveal that the computational complexity and memory requirement of our proposed architecture is very efficient compared to existing deep learning state-of-the-art methods for the task of nuclei segmentation of H&E stained histopathology images. The source code of our implementation will be available at https://github.com/Aaatresh/Kidney-SegNet. © 2021 Elsevier LtdItem A Robust CNN Framework for Change Detection Analysis From Bitemporal Remote Sensing Images(Institute of Electrical and Electronics Engineers Inc., 2024) Sravya, N.; Bhaduka, K.; Lal, S.; Nalini, J.; Chintala, C.S.—Deep learning (DL) algorithms are currently the most effective methods for change detection (CD) from high-resolution multispectral (MS) remote-sensing (RS) images. Because a variety of satellites are able to provide a lot of data, it is now easy to find changes using efficient DL models. Current CD methods focus on simple structure and combining the features obtained by all the stages together rather than extracting multiscale features from a single stage since it may lead to information loss and an imbalance contribution of features at different stages. This in turn results in misclassification of small changed areas and poor edge and shape preservation of changed areas. This article introduces an enhanced RSCD network (ERSCDNet) for CD from bitemporal aerial and MS images. The proposed encoder–decoder-based ERSCDNet model uses an attention-based encoder and decoder block and a modified new spatial pyramid pooling block at each stage of the decoder part, which effectively utilize features at each encoder stages and prevent information loss. The learning, vision, and remote sensing CD (LEVIR-CD), Onera satellite change detection (OSCD), and Sun Yat-Sen University CD (SYSU-CD) datasets are used to evaluate the ERSCDNet model. The ERSCDNet gives better performance than all the models used in this article for comparison. It gives an F1 score, a Kappa coefficient, and a Jaccard index of (0.9306, 0.9282, 0.8703), (0.8945, 0.8887, 0.8091), and (0.7581, 0.6876, 0.6103) on OSCD, LEVIR-CD, and SYSU-CD datasets, respectively. © 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.