Faculty Publications
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Item Multi-Modal Medical Image Fusion with Adaptive Weighted Combination of NSST Bands Using Chaotic Grey Wolf Optimization(Institute of Electrical and Electronics Engineers Inc., 2019) Asha, C.S.; Lal, S.; Gurupur, V.P.; Saxena, P.U.P.Recently, medical image fusion has emerged as an impressive technique in merging the medical images of different modalities. Certainly, the fused image assists the physician in disease diagnosis for effective treatment planning. The fusion process combines multi-modal images to incur a single image with excellent quality, retaining the information of original images. This paper proposes a multi-modal medical image fusion through a weighted blending of high-frequency subbands of nonsubsampled shearlet transform (NSST) domain via chaotic grey wolf optimization algorithm. As an initial step, the NSST is applied on source images to decompose into the multi-scale and multi-directional components. The low-frequency bands are fused based on a simple max rule to sustain the energy of an individual. The texture details of input images are preserved by an adaptively weighted combination of high-frequency images using a recent chaotic grey wolf optimization algorithm to minimize the distance between the fused image and source images. The entire process emphasizes on retaining the energy of the low-frequency band and the transferring of texture features from source images to the fused image. Finally, the fused image is formed using inverse NSST of merged low and high-frequency bands. The experiments are carried out on eight different disease datasets obtained from Brain Atlas, which consists of MR-T1 and MR-T2, MR and SPECT, MR and PET, and MR and CT. The effectiveness of the proposed method is validated using more than 100 pairs of images based on the subjective and objective quality assessment. The experimental results confirm that the proposed method performs better in contrast with the current state-of-the-art image fusion techniques in terms of entropy, VIFF, and FMI. Hence, the proposed method will be helpful for disease diagnosis, medical treatment planning, and surgical procedure. © 2013 IEEE.Item Efficient and robust deep learning architecture for segmentation of kidney and breast histopathology images(Elsevier Ltd, 2021) Chanchal, A.K.; Kumar, A.; Lal, S.; Kini, J.Image segmentation is consistently an important task for computer vision and the analysis of medical images. The analysis and diagnosis of histopathology images by using efficient algorithms that separate hematoxylin and eosin-stained nuclei was the purpose of our proposed method. In this paper, we propose a deep learning model that automatically segments the complex nuclei present in histology images by implementing an effective encoder–decoder architecture with a separable convolution pyramid pooling network (SCPP-Net). The SCPP unit focuses on two aspects: first, it increases the receptive field by varying four different dilation rates, keeping the kernel size fixed, and second, it reduces the trainable parameter by using depth-wise separable convolution. Our deep learning model experimented with three publicly available histopathology image datasets. The proposed SCPP-Net provides better experimental segmentation results compared to other existing deep learning models and is evaluated in terms of F1-score and aggregated Jaccard index. © 2021 Elsevier LtdItem Deep structured residual encoder-decoder network with a novel loss function for nuclei segmentation of kidney and breast histopathology images(Springer, 2022) Chanchal, A.K.; Lal, S.; Kini, J.To improve the process of diagnosis and treatment of cancer disease, automatic segmentation of haematoxylin and eosin (H & E) stained cell nuclei from histopathology images is the first step in digital pathology. The proposed deep structured residual encoder-decoder network (DSREDN) focuses on two aspects: first, it effectively utilized residual connections throughout the network and provides a wide and deep encoder-decoder path, which results to capture relevant context and more localized features. Second, vanished boundary of detected nuclei is addressed by proposing an efficient loss function that better train our proposed model and reduces the false prediction which is undesirable especially in healthcare applications. The proposed architecture experimented on three different publicly available H&E stained histopathological datasets namely: (I) Kidney (RCC) (II) Triple Negative Breast Cancer (TNBC) (III) MoNuSeg-2018. We have considered F1-score, Aggregated Jaccard Index (AJI), the total number of parameters, and FLOPs (Floating point operations), which are mostly preferred performance measure metrics for comparison of nuclei segmentation. The evaluated score of nuclei segmentation indicated that the proposed architecture achieved a considerable margin over five state-of-the-art deep learning models on three different histopathology datasets. Visual segmentation results show that the proposed DSREDN model accurately segment the nuclear regions than those of the state-of-the-art methods. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Non-subsampled Shearlet Domain-based De-speckling Framework for Optical Coherence Tomography Images(International Hellenic University - School of Science, 2023) Gupta, P.K.; Chanchal, A.K.; Lal, S.; Gupta, V.An effective instrument for obtaining an image of the retina is an optical coherence tomography (OCT) imaging device. OCT images of the retina are useful for diagnosing and tracking eye diseases. However, different physical configurations in the imaging apparatus are to blame for the speckle noise in retinal OCT images. The OCT image quality and assessment reliability are reduced due to aforementioned noise. This paper offered a paradigm for reducing speckle noise that was motivated by the mathematical formulation of speckle noise. Two distinct noise components make up speckle noise, one of which is additive and the other of which is multiplicative in nature. For each sort of noise, the suggested structure employs a different filter. To reduce the additive component of speckle noise, Weiner filtering is used. To minimize the multiplicative component of noise, a particular arrangement based on non-subsampled shearlet transform (NSST) is used. It is now widely acknowledge that NSST overcome the limitations of traditional wavelet transform therefore it very useful in dealing of distributed discontinuities therefore it is prefer in this research work.Real retinal OCT pictures are used to assess the proposed framework's quantitative and qualitative performance. The PSNR, MSE, SSIM, and CNR metrics are used to compare the suggested framework. In comparison to existing cutting-edge filters, the proposed framework performs better in terms of noise suppression capability with structure preservation capabilities. The proposed technique gives highest PSNR, SSIM and CNR value that indicate the effectiveness of proposed work in addition to this proposed work give lowest MSE value. The proposed work give better enhance images in comparison to other existing filter therefore it may be helpful to find out any abnormality in OCT image and improve the diagnose of OCT retinal image. © 2023 School of Science, IHU. All rights reserved.Item Two dimensional cuckoo search optimization algorithm based despeckling filter for the real ultrasound images(Springer Science and Business Media Deutschland GmbH, 2024) Gupta, P.K.; Lal, S.; Kiran, M.S.; Husain, F.A clinical ultrasound imaging plays a significant role in the proper diagnosis of patients because, it is a cost-effective and non-invasive technique in comparison with other methods. The speckle noise contamination caused by ultrasound images during the acquisition process degrades its visual quality, which makes the diagnosis task difficult for physicians. Hence, to improve their visual quality, despeckling filters are commonly used for processing of such images. However, several disadvantages of existing despeckling filters discourage the use of existing despeckling filters to reduce the effect of speckle noise. In this paper, two dimensional cuckoo search optimization algorithm based despeckling filter is proposed for avoiding limitations of various existing despeckling filters. Proposed despeckling filter is developed by combining fast non-local means filter and 2D finite impulse response (FIR) filter with cuckoo search optimization algorithm. In the proposed despeckling filter, the coefficients of 2D FIR filter are optimized by using the cuckoo search optimization algorithm. The quantitative results comparison between the proposed despeckling filter and other existing despeckling filters are analyzed by evaluating PSNR, MSE, MAE, and SSIM values for different real ultrasound images. Results reveal that the visual quality obtained by the proposed despeckling filter is better than other existing despeckling filters. The numerical results also reveal that the proposed despeckling filter is highly effective for despeckling the clinical ultrasound images. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.Item Evolution of LiverNet 2.x: Architectures for automated liver cancer grade classification from H&E stained liver histopathological images(Springer, 2024) Chanchal, A.K.; Lal, S.; Barnwal, D.; Sinha, P.; Arvavasu, S.; Kini, J.Recently, the automation of disease identification has been quite popular in the field of medical diagnosis. The rise of Convolutional Neural Networks (CNNs) for training and generalizing medical image data has proven to be quite efficient in detecting and identifying the types and sub-types of various diseases. Since the classification of large datasets of Hematoxylin & Eosin (H&E) stained histopathology images by experts can be expensive and time-consuming, automated processes using deep learning have been encouraged for the past decade. This paper introduces LiverNet 2.x model by modifying the previously encountered LiverNet architecture. The proposed model uses two different improvements of the Atrous Spatial Pyramid Pooling (ASPP) block to extract the clinically defined features of hepatocellular carcinoma (HCC) from liver histopathology images. LiverNet 2.0 uses a modified form of ASPP block known as DenseASPP, where all the atrous convolution outputs are densely connected. Whereas LiverNet 2.1 uses fewer concatenations while maintaining a large receptive field by stacking the dilated convolutional blocks in a tree-like fashion. This paper also discusses the trade-off between LiverNet 2.0 and LiverNet 2.1 in terms of accuracy and computational complexity. All comparison model and the proposed model is trained and tested on the patches of two different histopathological datasets. The experimental results show that the proposed model performs better compared to reference models. For the KMC Liver dataset, LiverNet 2.0 and LiverNet 2.1 achieved an accuracy of 97.50% and 97.14% respectively. Accuracy of 94.37% and 97.14% for the TCGA Liver dataset are achieved. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item FPGA implementation of deep learning architecture for kidney cancer detection from histopathological images(Springer, 2024) Lal, S.; Chanchal, A.K.; Kini, J.; Upadhyay, G.K.Kidney cancer is the most common type of cancer, and designing an automated system to accurately classify the cancer grade is of paramount importance for a better prognosis of the disease from histopathological kidney cancer images. Application of deep learning neural networks (DLNNs) for histopathological image classification is thriving and implementation of these networks on edge devices has been gaining the ground correspondingly due to high computational power and low latency requirements. This paper designs an automated system that classifies histopathological kidney cancer images. For experimentation, we have collected Kidney histopathological images of Non-cancerous, cancerous, and their respective grade of Renal Cell Carcinoma (RCC) from Kasturba Medical College (KMC), Mangalore, Karnataka, India. We have implemented and analyzed performances of deep learning architectures on a Field Programmable Gate Array (FPGA) board. Results yield that the Inception-V3 network provides better accuracy for kidney cancer detection as compared to other deep learning models on Kidney histopathological images. Further, the DenseNet-169 network provides better accuracy for kidney cancer grading as compared to other existing deep learning architecture on the FPGA board. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.Item Classification and grade prediction of kidney cancer histological images using deep learning(Springer, 2024) Chanchal, A.K.; N, S.; Lal, S.; Kumar, S.; Saxena, P.U.P.Renal Cell Carcinoma (RCC) is the most common malignant tumor (85%) of kidney cancer and has a complex histological pattern and nuclear structure. The manual diagnosis of kidney cancer or any other cancer from histopathology image depends on the knowledge and experience of pathologists, and the pathologist’s experience influences the results. According to studies, the kind of histology in kidney cancer is related to the prognosis and course of treatment. Since the kind of histology, molecular profile, and stage of the disease all affect how the disease is treated, there is an essential need to develop an automated system that can precisely analyze the histopathological images of the disease. This work demonstrates how a deep learning framework can be used to predict and classify associated grades of RCC from provided haematoxylin and eosin (H &E) images. The proposed model focuses on two important tasks- First to capture and extract associated features from the H &E images of five different grades. Second, to classify the new set of unseen H &E images into five separate grades using the obtained features. The proposed architecture has been tested and experimented on two independent datasets containing H &E stained histopathology images. The proposed architecture has been examined using the following performance metrics namely precision, recall, F1 - score, accuracy, Floating-point operations (FLOPs), and the total number of parameters. The obtained results show that the proposed architecture attains better results over seven state-of-the-art deep learning architectures on two different H &E stained histopathology image datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.