Browsing by Author "Lal, S."

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A framework for automated bone age assessment from digital hand radiographs
(Springer, 2020) Simu, S.; Lal, S.
Bone age assessment (BAA) is a method or technique that helps in predicting the age of a person whose age is unavailable and can also be used to find growth disorders if any. The automated bone age assessment system (ABAA) depends heavily on the efficiency of the feature extraction stage and the accuracy of a successive classification stage of the system. This paper has presented the implementation and analysis of feature extraction methods like Bag of features (BoF), Histogram of Oriented Gradients (HOG), and Texture Feature Analysis (TFA) methods on the segmented phalangeal region of interest (PROI) images and segmented radius-ulna region of interest (RUROI) images. Artificial Neural Networks (ANN) and Random Forest classifiers are used for evaluating classification problems. The experimental results obtained by BoF method for feature extraction along with Random Forest for classification have outperformed preceding techniques available in the literature. The mean error (ME) accomplished is 0.58 years and RMSE value of 0.77 years for PROI images and mean error of 0.53 years and RMSE of 0.72 years was achieved for RUROI images. Additionally results also proved that prior knowledge of gender of the person gives better results. The dataset contains radiographs of the left hand for an age range of 0-18 years. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
A Framework for Quality Enhancement of Multispectral Remote Sensing Images
(Institute of Electrical and Electronics Engineers Inc., 2018) Suresh, S.; Das, D.; Lal, S.
Researches in satellite image enhancement have been particularly confined to two major areas-contrast enhancement and image de noising of remote sensing images. The processing of relatively dark or shadowed images necessitates the need for robust remote sensing enhancement techniques. In this paper, a robust framework for quality enhancement of multispectral remote sensing images is proposed. The quantitative results of proposed algorithm and other existing remote sensing enhancement algorithms are calculated in terms of DE, NIQMC, BIQME, PisDist and CM on different remote sensing and other image databases. Results reveal that visual enhancement of the proposed algorithm is better than other existing remote sensing enhancement algorithms. Finally, the simulation experimental results show that proposed algorithm is effective and efficient for remotes sensing as well as natural images. Â© 2017 IEEE.
A metaheuristic framework based automated Spatial-Spectral graph for land cover classification from multispectral and hyperspectral satellite images
(Elsevier B.V., 2020) Suresh, S.; Lal, S.
Land cover classification of satellite images has been a very predominant area since the last few years. An increase in the amount of information acquired by satellite imaging systems, urges the need for automatic tools for classification. Satellite images exhibit spatial and/or temporal dependencies in which the conventional machine learning algorithms fail to perform well. In this paper, we propose an improved framework for automated land cover classification using Spatial Spectral Schroedinger Eigenmaps (SSSE) optimized by Cuckoo Search (CS) algorithm. Support Vector Machine (SVM) is adopted for the final thematic map generation following dimensionality reduction and clustering by the proposed approach. The novelty of the proposed framework is that the applicability of optimized SSSE for land cover classification of medium and high resolution multi-spectral satellite images is tested for the first time. The proposed method makes land cover classification system fully automatic by optimizing the algorithm specific image dependent parameter ? using CS algorithm. Experiments are carried out over publicly available high and medium resolution multi-spectral satellite image datasets (Landsat 5 TM and IKONOS 2 MS) and hyper-spectral satellite image datasets (Pavia University and Indian Pines) to assess the robustness of the proposed approach. Performance comparisons of the proposed method against state-of-the-art multi-spectral and hyper-spectral land cover classification methods reveal the efficiency of the proposed method. © 2020 Elsevier B.V.
A new deep learning architecture for dehazing of aerial remote sensing images
(Springer, 2022) Kalra, A.; Sequeira, A.; Manjunath, A.; Lal, S.; Raghavendra, R.
A major problem in most aerial remote image processing applications is the presence of haze in images. It is a phenomenon by which particles in the atmosphere disperse light, thus altering the quality of the overall image. This can be detrimental to the performance of vision-based algorithms such as those concerned with object detection. There have been numerous attempts using traditional image processing techniques as well as using deep learning approaches to eliminate this haze. In most cases, models tend to make assumptions on the nature of haze that are rarely true in reality. In this paper, we propose an end-to-end deep learning architecture that can dehaze aerial remote sensing images efficiently with minimal deviation from the ground truth. Many of the assumptions made in other models are eliminated and the relationship between hazed and dehazed images is directly computed. The proposed model is based on the observation that identifying structural and statistical portions separately from an image and using those features to reconstruct the image can give a realistic dehazed image. It also makes use of information exposed by different color spaces to achieve this using lesser computation. The experimental quantitative and qualitative results of the proposed architecture are compared with recent benchmark dehaze models on NYU hazy dataset and real-world hazy images. Experimental results yield that the proposed architecture outperforms benchmark models on test aerial remote sensing images. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
A Novel Adaptive Cuckoo Search Algorithm for Contrast Enhancement of Satellite Images
(Institute of Electrical and Electronics Engineers, 2017) Suresh, S.; Lal, S.; Chintala, C.S.; Kiran, M.S.
Owing to the increased demand for satellite images for various practical applications, the use of proper enhancement methods are inevitable. Visual enhancement of such images mainly focuses on improving the contrast of the scene procured, conserving its naturalness with minimum image artifacts. Last one decade traced an extensive use of metaheuristic approaches for automatic image enhancement processes. In this paper, a robust and novel adaptive Cuckoo search based Enhancement algorithm is proposed for the enhancement of various satellite images. The proposed algorithm includes a chaotic initialization phase, an adaptive Levy flight strategy and a mutative randomization phase. Performance evaluation is done by quantitative and qualitative results comparison of the proposed algorithm with other state-of-the-art metaheuristic algorithms. Box-and-whisker plots are also included for evaluating the stability and convergence capability of all the algorithms tested. Test results substantiate the efficiency and robustness of the proposed algorithm in enhancing a wide range of satellite images. © 2008-2012 IEEE.
A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images
(Nature Research, 2023) Chanchal, A.K.; Lal, S.; Kumar, R.; Kwak, J.T.; Kini, J.
Trends of kidney cancer cases worldwide are expected to increase persistently and this inspires the modification of the traditional diagnosis system to respond to future challenges. Renal Cell Carcinoma (RCC) is the most common kidney cancer and responsible for 80–85% of all renal tumors. This study proposed a robust and computationally efficient fully automated Renal Cell Carcinoma Grading Network (RCCGNet) from kidney histopathology images. The proposed RCCGNet contains a shared channel residual (SCR) block which allows the network to learn feature maps associated with different versions of the input with two parallel paths. The SCR block shares the information between two different layers and operates the shared data separately by providing beneficial supplements to each other. As a part of this study, we also introduced a new dataset for the grading of RCC with five different grades. We obtained 722 Hematoxylin & Eosin (H &E) stained slides of different patients and associated grades from the Department of Pathology, Kasturba Medical College (KMC), Mangalore, India. We performed comparable experiments which include deep learning models trained from scratch as well as transfer learning techniques using pre-trained weights of the ImageNet. To show the proposed model is generalized and independent of the dataset, we experimented with one additional well-established data called BreakHis dataset for eight class-classification. The experimental result shows that proposed RCCGNet is superior in comparison with the eight most recent classification methods on the proposed dataset as well as BreakHis dataset in terms of prediction accuracy and computational complexity. © 2023, The Author(s).
A novel solution to routing in Cognitive Radio ad-hoc networks in high primary user traffic environments
(Institute of Electrical and Electronics Engineers Inc., 2015) Sridhar, A.; Sridhar, S.; Lal, S.
Cognitive Radio technology holds great promise in solving the problem of spectrum scarcity. A plethora of routing protocols exist for Cognitive Radio networks, however most of them relay on establishing an end-to-end path using a Common Control Channel. This paper focuses on scenarios where the Primary User traffic is very high and erratic and therefore trying to set up end-to-end paths is not feasible. A novel solution to this problem is proposed where the cognitive users form a Cognitive Delay Tolerant Network through a modification in the network stack. Well researched delay tolerant networking routing protocols designed for networks with unreliable links, configured for multiple channel can used for routing in high primary user traffic environments. Through extensive simulation we show the that proposed architecture provides very high delivery ratio (close to 1) in the presence of very high primary user traffic with negligible computational complexity and the absence of a common control channel. We also show that trying to rely on routing protocols that try to establish end to end paths such as Multi-Channel AODV is not feasible. The performance of Multi-Channel AODV and proposed architecture is compared and analyzed with bundle/packet delivery ratio, end-to-end delay and hop count as performance metrics. Â© 2015 IEEE.
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.
A robust framework for de-speckling of optical coherence tomography images
(Science and Engineering Research Support Society ijbsbt@sersc.org PO Box 5014Sandy Bay TAS 7005 Tasmania, 2020) Gupta, P.K.; Lal, S.; Husain, F.
Recently, Optical Coherence Tomography (OCT) is emerging as a important diagnostic tool in medical application. OCT is widely used in detection of vision-related diseases. The analysis of retinal OCT images are very difficult due to speckle noise. The characteristic nature of this noise is multiplicative. A Number of de-speckling methods was proposed in the last few decades. All the existing de-speckling methods reduce the speckle noise, but these methods are not able to preserve the structure of the OCT image during de-speckling process. This paper propose a robust framework using wiener filter, soft thresholding and a weighted guided filter along with wavelet decomposition for the purpose of speckle noise reduction. The main contribution of this research is to remove speckle noise and preserve the structure of OCT image during de-noising process. In the first step of proposed framework, the original image is filtered by the wiener filter. After that a logarithmic transformation is used for the conversion of multiplicative noise into additive noise. Discrete wavelet transform (DWT) decompose the image into its constituents. Soft thresholding and weighted guided filter is used for high frequency sub-image and low frequency sub-image part respectively. In the last inverse DWT and antilog transformation are applied to acquire the de-noised image. Two different experiments are performed one on real OCT Images and other on natural images, to demonstrate the usefulness of the proposed framework. © 2020 SERSC.
A robust framework for quality enhancement of aerial remote sensing images
(Elsevier B.V., 2018) Karuna Kumari, E.; Das, D.; Suresh, S.; Lal, S.; Narasimhadhan, A.V.
This paper proposes a robust framework for quality restoration of remotely sensed aerial images. Proposed framework works in three steps: (1) Efficient color balancing and saturation adjustment, (2) Efficient color restoration, (3) Modified contrast enhancement using particle swarm optimization (PSO). In order to show the robustness, step-wise results of proposed framework is illustrated. Several aerial images from two publically available datasets are tested to support the robustness of the proposed framework over existing image quality restoration methods. The experimental results of proposed framework and other existing quality restoration methods are compared in terms of NIQMC, BIQME, MICHELSON, DE, EME and PIXDIST along with visual experimental results. Based on experimental results conducted on several aerial images suggest that the proposed framework is outperform over existing quality restoration methods. © 2018 Elsevier B.V.
A robust framework for visibility enhancement of foggy images
(Elsevier B.V., 2019) Pal, N.S.; Lal, S.; Shinghal, K.
This paper presents a robust framework for visibility enhancement of images degraded by foggy weather conditions. The proposed defogged algorithm is developed by the combining of modified visibility restoration approach and S-shaped transfer function for foggy weather degraded images. The proposed defogged algorithm works in two steps: in first step trilateral filter based visibility restoration algorithm is used for visibility and smoothness of the degraded images. Further in the second step S-shaped transfer function is used for contrast enhancement of the foggy images. The image quality metrics of proposed defogged and other existing visibility restoration algorithms are evaluated in terms of Fog Reduction Factor (FRF), Measure of Enhancement Factor (EMF) and blind parameter (?) on different foggy image databases. We demonstrate the strength of proposed defogged algorithm by estimation the thickness of fog in the input image as well as the output image. Finally, the simulation results and visualisation of defogged images indicate that the proposed defogged algorithm is highly effective and efficient for visibility enhancement of foggy images. © 2018 Karabuk University
A robust method for nuclei segmentation of HE stained histopathology images
(Institute of Electrical and Electronics Engineers Inc., 2020) Lal, S.; Desouza, R.; Maneesh, M.; Kanfade, A.; Kumar, A.; Perayil, G.; Alabhya, K.; Chanchal, A.K.; Kini, J.
Segmentation of histopathology images is an initial and vital step for image understanding. To increase the throughput and to maintain high accuracy, we have to go for an automatic image segmentation method. Here, a robust method for segmentation of cell nuclei in Hematoxylin and Eosin (HE) stained histopathology images is proposed. The proposed segmentation step consists of an initial pre-processing step containing adaptive colour de-convolution and a succession of morphological operations, followed by multilevel thresholding and post-processing steps. Minimum region size is the one parameter which is necessary for this method and set according to the resolution of histopathology image. The proposed nuclei segmentation method does not require any assumptions or prior information about cell morphology. Hence, proposed method applies to the analysis of a wide range of tissues such as liver, kidney, breast, gastric mucosa, and bone marrow and HE stained liver histopathology images from the Hospital. Results yield that proposed nuclei segmentation provides better results in terms of quantitatively and qualitatively on two datasets. Â© 2020 IEEE.
A robust visibility restoration framework for rainy weather degraded images
(UIKTEN - Association for Information Communication Technology Education and Science temjournal@gmail.com, 2018) Pal, N.S.; Lal, S.; Shinghal, K.
Visibility restoration of color rainy images is inevitable task for the researchers in many vision based applications. Rain produces a visual impact on image, so that the intensity and visibility of image is low. Therefore, there is a need to develop a robust visibility restoration algorithm for the rainy images. In this paper we proposed a robust visibility restoration framework for the images captured in rainy weather. The framework is the combined form of convolution neural network for rain removal and low light image enhancement for low contrast. The output results of the proposed framework and other latest de-rainy algorithms are estimated in terms of PSNR, SSIM and UIQI on rainy image from different databases. The quantitative and qualitative results of the proposed framework are better than other de-rainy algorithms. Finally, the obtained visualization result also shows the efficiency of the proposed framework. © 2018 Narendra Singh Pal, Shyam Lal, Kshitij Shinghall; published by UIKTEN.
A study about color normalization methods for histopathology images
(Elsevier Ltd, 2018) Roy, S.; Kumar Jain, A.K.; Lal, S.; Kini, J.R.
Histopathology images are used for the diagnosis of the cancerous disease by the examination of tissue with the help of Whole Slide Imaging (WSI) scanner. A decision support system works well by the analysis of the histopathology images but a lot of problems arise in its decision. Color variation in the histopathology images is occurring due to use of the different scanner, use of various equipments, different stain coloring and reactivity from a different manufacturer. In this paper, detailed study and performance evaluation of color normalization methods on histopathology image datasets are presented. Color normalization of the source image by transferring the mean color of the target image in the source image and also to separate stain present in the source image. Stain separation and color normalization of the histopathology images can be helped for both pathology and computerized decision support system. Quality performances of different color normalization methods are evaluated and compared in terms of quaternion structure similarity index matrix (QSSIM), structure similarity index matrix (SSIM) and Pearson correlation coefficient (PCC) on various histopathology image datasets. Our experimental analysis suggests that structure-preserving color normalization (SPCN) provides better qualitatively and qualitatively results in comparison to the all the presented methods for breast and colorectal cancer histopathology image datasets. © 2018 Elsevier Ltd
A study about evolutionary and non-evolutionary segmentation techniques on hand radiographs for bone age assessment
(Elsevier Ltd, 2017) Simu, S.; Lal, S.
In this paper, a study and performance comparison of various evolutionary and non-evolutionary segmentation techniques on digital hand radiographs for bone age assessment is presented. The segmented hand bones are of vital importance in process of automated bone age assessment (ABAA). Bone age assessment is a technique of checking the skeletal development and detecting growth disorder in a person. However, it is very difficult to segment out the bone from the soft tissue. The problem arises from overlapping pixel intensities between bone region and soft tissue region and also between soft tissue region and background. Thus there is a requirement for a robust segmentation technique for hand bone segmentation. Taking this into consideration we make a comparison between non-evolutionary and evolutionary segmentation algorithms implemented on hand radiographs to recognize bone borders and shapes. The simulation and experimental results demonstrate that multiplicative intrinsic component optimization (MICO) algorithm provides better results as compared to other existing evolutionary and non-evolutionary algorithms. © 2016 Elsevier Ltd
A Visibility Restoration Framework for rainy images by using Lo gradient minimization and Bilateral Filtering
(Institute of Electrical and Electronics Engineers Inc., 2018) Pal, N.S.; Lal, S.; Shinghal, K.
In this paper we proposed an algorithm for the visibility restoration of color rainy images. Rain drops affect the visual impact on image so that the visibility and intensity of the captured images are very low. We use a L0 gradient minimization technique for removing the rain pixels. L0 gradient minimization technique globally control the non zero gradient, so that low amplitude and insignificant detail are diminished but salient edges of the images are preserved. First we apply this refining illumination techniques on our rainy image for removing the rain and Secondly we apply a bilateral filtering for smoothing the color image for human perception. The quantitative results of proposed algorithm and other existing rainy algorithms for color rainy images are obtained in terms peak to peak signal to noise ratio (PSNR), universal image quality index(UIQI) and structure similarity index measurement (SSIM) and on different color rainy images from databases. The visual quality of the proposed algorithm also better than other existing rainy algorithms. Finally, the simulation result shows that the proposed algorithm is highly effective and efficient for visibility restoration of rainy images. Â© 2018 IEEE.
An Effective Deep Learning Model for Pan-Sharpening of Satellite Images
(Institute of Electrical and Electronics Engineers Inc., 2024) Telang, S.; Basavaraju, K.S.; Sravya, N.; Lal, S.
Image fusion techniques are widely used to enhance images by combining two or more remote sensing images. The fusion task of "pan-sharpening"is to merge low resolution Multispectral (MS) and High resolution Panchromatic (PAN) satellite images of the same scene obtained by the same satellite. This paper presents proposed an effective deep learning model leveraging a combination of novel techniques for feature enhancement and aggregation. The proposed model named as Efficient Non-local Feature Enhancement Network (ENFE-Net) integrates the PAN guided band-aware feature enhancement module with an Efficient Non-local Attention (ENLA) mechanism and Spectral Aggregation Module (SpecAM). The PAN guided band-aware feature enhancement module facilitates effective feature extraction, leverages PAN features to conduct band-aware multi-spectral feature modulation, selectively enhancing the information of each spectral band. Additionally, the integration of the ENLA mechanism enables the model to capture similar contextual dependencies in the input data efficiently, enhancing its discriminative power. Furthermore, the SpecAM is employed to aggregate spectral information effectively, improving the model's effectiveness to adjust the spectral information. Performance of proposed ENFE-Net model is evaluated on PAirMax datasets and demonstrate its superior performance compared to existing traditional and recent deep learning methods. Experimental results of proposed ENFE-Net model show significant improvements over existing pan-sharpening methods. Â© 2024 IEEE.
An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions
(Elsevier Ltd, 2016) Suresh, S.; Lal, S.
Satellite image segmentation is challenging due to the presence of weakly correlated and ambiguous multiple regions of interest. Several bio-inspired algorithms were developed to generate optimum threshold values for segmenting such images efficiently. Their exhaustive search nature makes them computationally expensive when extended to multilevel thresholding. In this paper, we propose a computationally efficient image segmentation algorithm, called CSMcCulloch, incorporating McCulloch's method for lévy flight generation in Cuckoo Search (CS) algorithm. We have also investigated the impact of Mantegna?s method forlévy flight generation in CS algorithm (CSMantegna) by comparing it with the conventional CS algorithm which uses the simplified version of the same. CSMantegna algorithm resulted in improved segmentation quality with an expense of computational time. The performance of the proposed CSMcCulloch algorithm is compared with other bio-inspired algorithms such as Particle Swarm Optimization (PSO) algorithm, Darwinian Particle Swarm Optimization (DPSO) algorithm, Artificial Bee Colony (ABC) algorithm, Cuckoo Search (CS) algorithm and CSMantegna algorithm using Otsu's method, Kapur entropy and Tsallis entropy as objective functions. Experimental results were validated by measuring PSNR, MSE, FSIM and CPU running time for all the cases investigated. The proposed CSMcCulloch algorithm evolved to be most promising, and computationally efficient for segmenting satellite images. Convergence rate analysis also reveals that the proposed algorithm outperforms others in attaining stable global optimum thresholds. The experiments results encourages related researches in computer vision, remote sensing and image processing applications. © 2016 Elsevier Ltd. All rights reserved.
An efficient method for contrast enhancement of real world hyper spectral images
(Zarka Private Univ PO Box 132222 ZARQA 13132, 2015) Lal, S.; Nishad, R.
This paper proposed an efficient method for contrast enhancement of real world hyper spectral images. The contrast of image is an important characteristic by which the quality of image can be judged as good or poor quality. The proposed method is consists of two stages: In first stage the poor quality of image is process by automatic contrast adjustment in spatial domain and in second stage the output of first stage is further process by adaptive filtering for image enhancement in frequency domain. Simulation and experimental results on benchmark real world hyper spectral image database demonstrates that proposed method provides better results as compared to other state-of-art contrast enhancement techniques. Proposed method performs better in different dark and bright real world hyper spectral images by adjusting their contrast very frequently. Proposed method is very simple and efficient approach for contrast enhancement of real world hyper spectral images. This method can be used in different applications where images are suffering from different contrast problems. © 2015, Zarka Private Univ. All rights reserved.
An Efficient Parallel Branch Network for Multi-Class Classification of Prostate Cancer From Histopathological Images
(John Wiley and Sons Inc, 2025) Srivastava, V.; Prabhu, A.; Sravya, S.; Vibha Damodara, K.; Lal, S.; Kini, J.
Prostate cancer is one of the prevalent forms of cancer, posing a significant health concern for men. Accurate detection and classification of prostate cancer are crucial for effective diagnosis and treatment planning. Histopathological images play a pivotal role in identifying prostate cancer by enabling pathologists to identify cellular abnormalities and tumor characteristics. With the rapid advancements in deep learning, Convolutional Neural Networks (CNNs) have emerged as a powerful tool for tackling complex computer vision tasks, including object detection, classification, and segmentation. This paper proposes a Parallel Branch Network (PBN), a CNN architecture specifically designed for the automatic classification of prostate cancer into its subtypes from histopathological images. The paper introduces a novel Efficient Residual (ER) block that enhances feature representation using residual learning and multi-scale feature extraction. By utilizing multiple branches with different filter reduction ratios and dense attention mechanisms, the block captures diverse features while preserving essential information. The proposed PBN model achieved a classification accuracy of 93.16% on the Prostate Gleason dataset, outperforming all other comparison models. © 2025 Wiley Periodicals LLC.