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7th International Conference on Computer Vision and image processing, CVIP 2022

作者： Innani, Shubham Dutande, Prasad Baheti, Bhakti Baid, Ujjwal Talbar, Sanjay Center of Excellence Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India

ISBN: (纸本)9783031314063

Patients diagnosed with skin cancer like melanoma are prone to a high mortality rate. Automatic lesion analysis is critical in skin can¬cer diagnosis and ensures effective treatment. The computer-aided diag¬nosis of such skin cancer in dermoscopic images can significantly reduce the clinicians’ workload and help improve diagnostic accuracy. Although researchers are working extensively to address this problem, early detec¬tion and accurate identification of skin lesions remain challenging. This research focuses on a two-step framework for skin lesion segmentation followed by classification for lesion analysis. We explored the effective¬ness of deep convolutional neural network (CNN) based architectures by designing an encoder-decoder architecture for skin lesion segmenta¬tion and CNN based classification network. The proposed approaches are evaluated quantitatively in terms of the Accuracy, mean Intersection over Union(mIoU) and Dice Similarity Coefficient. Our cascaded end-to-end deep learning-based approach is the first of its kind, where the classification accuracy of the lesion is significantly improved because of prior segmentation. The code is available at https://***/shubhaminnani/skin/lesion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： image enhancement

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Modified ResNet for Volumetric Segmentation of Liver and Its Tumor from 3D CT

Modified ResNet for Volumetric Segmentation of Liver and Its...

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International Conference on Artificial Intelligence and Sustainable Engineering, AISE 2020

作者： Mourya, Gajendra Kumar Bhatia, Dinesh Gogoi, Manashjit Talbar, S.N. Baid, Ujjwal Dudante, Prasad Department of Biomedical Engineering North-Eastern Hill University Meghalaya Shillong India Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India

ISBN: (纸本)9789811685415

Liver tumor segmentation is the first step to investigate severity of liver disorder. The conventional automatic segmentation methods highly depend on hand-crafted features and priori information, which are tedious and time consuming for medical images. Fully convolutional network (FCN) of the deep learning model can extract features automatically and hierarchically learning concerning layers depth. In this article, we demonstrated the application of modified ResNet for the automatic segmentation of liver and tumor segmentation from 3D CT medical imaging datasets. The FCN ResNet architecture is modified while changing the levels at encoder and decoder ends to overcome the limitation of the conventional segmentation technique. The network input and output image dimension kept the same for training and testing for the designed model. LiTS and 3Dirac data use for training and testing the model. Multifold training datasets strategy utilized. Results were evaluated quantitatively and achieved DICE 0.96 and 0.81 for liver and tumor, respectively. The modified technique will assist to interventional radiologist workflow and faster the diagnostic and therapeutic procedures. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computerized tomography

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Generative Adversarial Networks based Skin Lesion Segmentation

arXiv

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arXiv 2023年

作者： Innani, Shubham Dutande, Prasad Baid, Ujjwal Pokuri, Venu Bakas, Spyridon Talbar, Sanjay Baheti, Bhakti Guntuku, Sharath Chandra Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia United States Department of Computer and Information Science University of Pennsylvania Philadelphia United States

Skin cancer is a serious condition that requires accurate diagnosis and treatment. One way to assist clinicians in this task is using computer-aided diagnosis (CAD) tools that automatically segment skin lesions from dermoscopic images. We propose a novel adversarial learning-based framework called Efficient-GAN (EGAN) that uses an unsupervised generative network to generate accurate lesion masks. It consists of a generator module with a top-down squeeze excitation-based compound scaled path, an asymmetric lateral connection-based bottom-up path, and a discriminator module that distinguishes between original and synthetic masks. A morphology-based smoothing loss is also implemented to encourage the network to create smooth semantic boundaries of lesions. The framework is evaluated on the International Skin Imaging Collaboration (ISIC) Lesion Dataset 2018. It outperforms the current state-of-the-art skin lesion segmentation approaches with a Dice coefficient, Jaccard similarity, and Accuracy of 90.1%, 83.6%, and 94.5%, respectively. We also design a lightweight segmentation framework (MGAN) that achieves comparable performance as EGAN but with an order of magnitude lower number of training parameters, thus resulting in faster inference times for low compute resource settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Laser Scar Classification in Retinal Fundus images Using Wavelet Transform and Local Variance 1st

Laser Scar Classification in Retinal Fundus Images Using Wav...

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1st International Symposium on Computer Vision and Machine Intelligence in Medical image Analysis, ISCMM 2019 in conjunction with the 2nd International Conference on Advanced Computational and Communication Paradigms, ICACCP 2019

作者： Raut, Rashmi Sapate, Visharad Rokde, Abhay Pachade, Samiksha Porwal, Prasanna Kokare, Manesh Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India

ISBN: (纸本)9789811387975

Diabetic retinopathy (DR) affects the vision of the person and may eventually lead to blindness. In the initial stage of the disease, patients are treated with a laser to restrict its progression. Such laser treatment leaves behind scars on the retina and patients are advised to undergo screening regularly to track further complications. This paper presents a novel retinal background characterization approach that explores the potential of discrete wavelet transform and rotational-invariant variance features for texture classification of retinal images with and without laser marks. For this experiment, different classifiers, namely, support vector machine, naive Bayes, neural network, and random forest classifiers are tested. We used two publicly available datasets, namely, LMD-DRS and LMD-BAPT. In all cases, the proposed approach obtained the sensitivity, specificity, and accuracy values higher than 68.9%, 70.2%, and 69.4%, respectively. It was found that all performance measures achieve over 87.5, 89.4, and 86.7% for the classification task using random forest classifier. These promising results suggest that the proposed technique can discriminate retinal images having laser marks and without laser marks, and has the potential to be an important constituent in computerized screening solution for retinal images. © Springer Nature Singapore Pte Ltd. 2020

关键词： Discrete wavelet transforms

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Eff-UNet: A Novel Architecture for Semantic Segmentation in Unstructured Environment

Eff-UNet: A Novel Architecture for Semantic Segmentation in ...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Bhakti Baheti Shubham Innani Suhas Gajre Sanjay Talbar SGGSIET Center of Excellence in Signal and Image Processing Nanded India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Since the last few decades, the number of road causalities has seen continuous growth across the globe. Nowa-days intelligent transportation systems are being developed to enable safe and relaxed driving and scene understanding of the surrounding environment is an integral part of it. While several approaches are being developed for semantic scene segmentation based on deep learning and Convolutional Neural Network (CNN), these approaches assume well structured road infrastructure and driving environment. We focus our work on recent India Driving Lite Dataset (IDD), which contains data from unstructured driving environment and was hosted as an online challenge in NCVPRIPG 2019. We propose a novel architecture named as Eff-UNet which combines the effectiveness of compound scaled EfficientNet as the encoder for feature extraction with UNet decoder for reconstructing the fine-grained segmentation map. High level feature information as well as low level spatial information useful for precise segmentation are combined. The proposed architecture achieved 0.7376 and 0.6276 mean Intersection over Union (mIoU) on validation and test dataset respectively and won first prize in IDD lite segmentation challenge outperforming other approaches in the literature.

关键词： image segmentation Semantics Roads Feature extraction Decoding image resolution Computer architecture

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Communicating radar using frequency-shift keying and fractional Fourier transform for automotive applications

Communicating radar using frequency-shift keying and fractio...

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IET International Radar Conference 2018, IRC 2018

作者： Striano, Pasquale Ilioudis, Christos V. Clemente, Carmine Soraghan, John J. Center for Excellence in Signal and Image Processing University of Stratclyde Glasgow United Kingdom

Modern radar applications, such as automotive, integration of radar and communications, have become an important requirement. Systems able to provide joint radar and communications would exploit the same hardware while providing enhanced capabilities to the carrying platform, such as road situation awareness and content sharing. In this article, a novel framework for automotive radars based on the fractional Fourier transform (FrFT) is introduced. The aim of the proposed approach is to preserve radar performance while integrating a communication link. Simulation results demonstrate the potential of the proposed framework for automotive applications. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Automobiles

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Detection of Pathological Myopia and Optic Disc Segmentation with Deep Convolutional Neural Networks

Detection of Pathological Myopia and Optic Disc Segmentation...

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IEEE Region 10 International Conference TENCON

作者： Ujjwal Baid Bhakti Baheti Prasad Dutande Sanjay Talbar Center of Excellence in Signal and Image Processing SGGS Institute of Engineering and Technology Nanded Maharashtra India

Pathological Myopia (PM) is one of the leading cause of blindness all over the world and is particularly prevalent in Asia. Pathological myopia is a severe case of myopia which is accompanied by degenerative changes in the retina and can lead to non recoverable vision loss. The accurate diagnosis of PM will enable timely intervention and better treatment planning. Current methods for the detection of pathological myopia are manual and subjective. In this study, we have developed a Convolutional Neural Network (CNN) based system to estimate probability/risk of the patient to be diagnosed with PM from his retinal images. We have also segmented optic disc from the retinal images with novel Residual UNet architecture. Validation data results are evaluated from the online server and achieved 0.9973 and 0.901 for the task of PM classification and optic disc segmentation respectively.

关键词： image segmentation Optical imaging Pathology Computer architecture Retina Adaptive optics Optical computing

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Semantic Scene Understanding in Unstructured Environment with Deep Convolutional Neural Network

Semantic Scene Understanding in Unstructured Environment wit...

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IEEE Region 10 International Conference TENCON

作者： Bhakti Baheti Suhas Gajre Sanjay Talbar Center of Excellence in Signal and Image Processing SGGS Institute of Engineering and Technology Nanded Maharashtra India

Number of road fatalities have been continuously increasing since last few decades all over the world. Nowadays advanced driver assistance systems are being developed to help the driver in driving process and semantic scene understanding is an essential task for it. Convolutional Neural Networks (CNN) have shown impressive progress in various computer vision tasks including the semantic segmentation. Various architectures have been proposed in literature but loss of spatial acuity in semantic segmentation prevents them from achieving better results as details of small objects are lost in downsampling. To overcome this drawback, we propose to use dilated residual network as backbone in DeepLabV3+ which enables to preserve the details of smaller objects in the scene without reducing the receptive field. We focus our work on India Driving dataset (IDD) containing data from unstructured traffic scenario. Proposed architecture proves to be effective compared to earlier approaches in literature with 0.618 mIoU.

关键词： Semantics image segmentation Roads Task analysis Convolutional neural networks image resolution Decoding

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2nd International Conference on Computer Vision and image processing, CVIP 2017

作者： Baheti, Bhakti Kutty, Krishnan Gajre, Suhas Talbar, Sanjay Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology NandedMaharashtra India Tata Consultancy Services PuneMaharashtra India

ISBN: (纸本)9789811078972

We present a generic vehicle detection approach using a single query image of vehicle to find similar objects in the test image. The proposed method is without any prior training or segmentation of the test image. The approach is based on computing local self-similarity (LSS) descriptors from query and test images that capture local internal geometric layout within the image. Descriptors from query and test image are matched as two-stage process in sliding window framework. In order to exploit usefulness of LSS for generic object detection, we make following contributions: (i) we present few novel ideas to discard the non-informative descriptors to reduce computational expense in feature matching. (ii) We propose a deformation tolerant version of sliding window-based matching framework rather than point-to-point matching. (iii) We also show that selection of landmark points from the query not only makes the algorithm faster but also improves the performance of detection. We evaluate our results on UIUC car dataset, and results clearly outperform earlier training free methods with 91% accuracy. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Object detection

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image Retrieval Based on Wavelet and Optimized Local Gaussian Difference Extrema Pattern

Image Retrieval Based on Wavelet and Optimized Local Gaussia...

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IEEE International Conference on Industrial and Information Systems

作者： Farha Nausheen Ravi Kamble Manesh Kokare Center of Excellence in Signal and Image processing SGGS Institute of Electronics and Telecom. Engg Nanded India

ISBN: (纸本)9781538684931

An effective extraction of image features is very important in Content based image retrieval (CBIR). This paper presents a new CBIR scheme based on optimized combination of two texture features to increase retrieval performance. The proposed method uses wavelet and Local Gaussian Difference Extrema Pattern (LGDEP) as texture feature effectively. Performance of proposed system is formalized on different databases like: MIT-VisTex, Corel-1k. In addition, the experimentation carried for medical databases like: MESSIDOR (Retinal images), Magnetic Resonance Imaging (OASIS-MRI) database, VIA/I-ELCAP-CT lung database. The results shows substantial improvement in terms of average precision rate (APR) like: 92% to 99% for MIT-VisTex, 80% to 83% for MESSIDOR, etc.

关键词： image retrieval Feature extraction Biomedical imaging Magnetic resonance imaging Wavelet transforms Conferences

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