PhD (Image Processing and Machine learning) - Indian Statistical Institute Kolkata (Degree Awarded by University of Calcutta)
BE (Electronics)- University of Pune
A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images Elima Hussain, Lipi B. Mahanta, Chandana Ray Das, Manjula Choudhury, Manish Chowdhury Artificial Intelligence in Medicine, 2020 Pap smear is often employed as a screening test for diagnosing cervical pre-cancerous and cancerous lesions. Accurate identification of dysplastic changes amongst the cervical cells in a Pap smear image is thus essential for rapid diagnosis and prognosis. Manual pathological observations used in clinical practice require exhaustive analysis of thousands of cell nuclei in a whole slide image to visualize the dysplastic nuclear changes which make the process tedious and time-consuming. Automated nuclei segmentation and classification exist but are challenging to overcome issues like nuclear intra-class variability and clustered nuclei separation. To address such challenges, we put forward an application of instance segmentation and classification framework built on an Unet architecture by adding residual blocks, densely connected blocks and a fully convolutional layer as a bottleneck between encoder-decoder blocks for Pap smear images. The number of convolutional layers in the standard Unet has been replaced by densely connected blocks to ensure feature reuse-ability property while the introduction of residual blocks in the same attempts to converge the network more rapidly. The framework provides simultaneous nuclei instance segmentation and also predicts the type of nucleus class as belonging to normal and abnormal classes from the smear images. It works by assigning pixel-wise labels to individual nuclei in a whole slide image which enables identifying multiple nuclei belonging to the same or different class as individual distinct instances. Introduction of a joint loss function in the framework overcomes some trivial cell level issues on clustered nuclei separation. To increase the robustness of the overall framework, the proposed model is preceded with a stacked auto-encoder based shape representation learning model. The proposed model outperforms two state-of-the-art deep learning models Unet and Mask_RCNN with an average Zijdenbos similarity index of 97 % related to segmentation along with binary classification accuracy of 98.8 %. Experiments on hospital-based datasets using liquid-based cytology and conventional pap smear methods along with benchmark Herlev datasets proved the superiority of the proposed method than Unet and Mask_RCNN models in terms of the evaluation metrics under consideration.
Video shot boundary detection using multiscale geometric analysis of nsct and least squares support vector machine Jaydeb Mondal, Malay Kumar Kundu, Sudeb Das, Manish Chowdhury Multimedia Tools and Applications, 2018 The fundamental step in video content analysis is the temporal segmentation of video stream into shots, which is known as Shot Boundary Detection (SBD). The sudden transition from one shot to another is known as Abrupt Transition (AT), whereas if the transition occurs over several frames, it is called Gradual Transition (GT). A unified framework for the simultaneous detection of both AT and GT have been proposed in this article. The proposed method uses the multiscale geometric analysis of Non-Subsampled Contourlet Transform (NSCT) for feature extraction from the video frames. The dimension of the feature vectors generated using NSCT is reduced through principal component analysis to simultaneously achieve computational efficiency and performance improvement. Finally, cost efficient Least Squares Support Vector Machine (LS-SVM) classifier is used to classify the frames of a given video sequence based on the feature vectors into No-Transition (NT), AT and GT classes. A novel efficient method of training set generation is also proposed which not only reduces the training time but also improves the performance. The performance of the proposed technique is compared with several state-of-the-art SBD methods on TRECVID 2007 and TRECVID 2001 test data. The empirical results show the effectiveness of the proposed algorithm.
Automatic brain segmentation using artificial neural networks with shape context Amirreza Mahbod, Manish Chowdhury, Örjan Smedby, Chunliang Wang Pattern Recognition Letters, 2018 Segmenting brain tissue from MR scans is thought to be highly beneficial for brain abnormality diagnosis, prognosis monitoring, and treatment evaluation. Many automatic or semi-automatic methods ha ...
Interactive radiographic image retrieval system Malay Kumar Kundu, Manish Chowdhury, Sudeb Das Computer Methods and Programs in Biomedicine, 2017 BACKGROUND AND OBJECTIVE Content based medical image retrieval (CBMIR) systems enable fast diagnosis through quantitative assessment of the visual information and is an active research topic over the past few decades. Most of the state-of-the-art CBMIR systems suffer from various problems: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering schemes. Inability to properly handle the "semantic gap" and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database). This yields an exigent demand for developing highly effective and computationally efficient retrieval system. METHODS We propose a novel interactive two-stage CBMIR system for diverse collection of medical radiographic images. Initially, Pulse Coupled Neural Network based shape features are used to find out the most probable (similar) image classes using a novel "similarity positional score" mechanism. This is followed by retrieval using Non-subsampled Contourlet Transform based texture features considering only the images of the pre-identified classes. Maximal information compression index is used for unsupervised feature selection to achieve better results. To reduce the semantic gap problem, the proposed system uses a novel fuzzy index based relevance feedback mechanism by incorporating subjectivity of human perception in an analytic manner. RESULTS Extensive experiments were carried out to evaluate the effectiveness of the proposed CBMIR system on a subset of Image Retrieval in Medical Applications (IRMA)-2009 database consisting of 10,902 labeled radiographic images of 57 different modalities. We obtained overall average precision of around 98% after only 2-3 iterations of relevance feedback mechanism. We assessed the results by comparisons with some of the state-of-the-art CBMIR systems for radiographic images. CONCLUSIONS Unlike most of the existing CBMIR systems, in the proposed two-stage hierarchical framework, main importance is given on constructing efficient and compact feature vector representation, search-space reduction and handling the "semantic gap" problem effectively, without compromising the retrieval performance. Experimental results and comparisons show that the proposed system performs efficiently in the radiographic medical image retrieval field.
Granulometry-based trabecular bone segmentation Manish Chowdhury, Benjamin Klintström, Eva Klintström, Örjan Smedby, Rodrigo Moreno Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017 The accuracy of the analyses for studying the three dimensional trabecular bone microstructure rely on the quality of the segmentation between trabecular bone and bone marrow. Such segmentation is challenging for images from computed tomography modalities that can be used in vivo due to their low contrast and resolution. For this purpose, we propose in this paper a granulometry-based segmentation method. In a first step, the trabecular thickness is estimated by using the granulometry in gray scale, which is generated by applying the opening morphological operation with ball-shaped structuring elements of different diameters. This process mimics the traditional sphere-fitting method used for estimating trabecular thickness in segmented images. The residual obtained after computing the granulometry is compared to the original gray scale value in order to obtain a measurement of how likely a voxel belongs to trabecular bone. A threshold is applied to obtain the final segmentation. Six histomorphometric parameters were computed on 14 segmented bone specimens imaged with cone-beam computed tomography (CBCT), considering micro-computed tomography (micro-CT) as the ground truth. Otsu’s thresholding and Automated Region Growing (ARG) segmentation methods were used for comparison. For three parameters (Tb.N, Tb.Th and BV/TV), the proposed segmentation algorithm yielded the highest correlations with micro-CT, while for the remaining three (Tb.Nd, Tb.Tm and Tb.Sp), its performance was comparable to ARG. The method also yielded the strongest average correlation (0.89). When Tb.Th was computed directly from the gray scale images, the correlation was superior to the binary-based methods. The results suggest that the proposed algorithm can be used for studying trabecular bone in vivo through CBCT.
Segmentation of cortical bone using fast level sets Manish Chowdhury, Daniel Jörgens, Chunliang Wang, Örjan Smedby, Rodrigo Moreno Progress in Biomedical Optics and Imaging Proceedings of SPIE, 2017 Cortical bone plays a big role in the mechanical competence of bone. The analysis of cortical bone requires accurate segmentation methods. Level set methods are usually in the state-of-the-art for segmenting medical images. However, traditional implementations of this method are computationally expensive. This drawback was recently tackled through the so-called coherent propagation extension of the classical algorithm which has decreased computation times dramatically. In this study, we assess the potential of this technique for segmenting cortical bone in interactive time in 3D images acquired through High Resolution peripheral Quantitative Computed Tomography (HR-pQCT). The obtained segmentations are used to estimate cortical thickness and cortical porosity of the investigated images. Cortical thickness and Cortical porosity is computed using sphere fitting and mathematical morphological operations respectively. Qualitative comparison between the segmentations of our proposed algorithm and a previously published approach on six images volumes reveals superior smoothness properties of the level set approach. While the proposed method yields similar results to previous approaches in regions where the boundary between trabecular and cortical bone is well defined, it yields more stable segmentations in challenging regions. This results in more stable estimation of parameters of cortical bone. The proposed technique takes few seconds to compute, which makes it suitable for clinical settings.
Automated classification of Pap smear images to detect cervical dysplasia Kangkana Bora, Manish Chowdhury, Lipi B. Mahanta, Malay Kumar Kundu, Anup Kumar Das Computer Methods and Programs in Biomedicine, 2017 BACKGROUND AND OBJECTIVES The present study proposes an intelligent system for automatic categorization of Pap smear images to detect cervical dysplasia, which has been an open problem ongoing for last five decades. METHODS The classification technique is based on shape, texture and color features. It classifies the cervical dysplasia into two-level (normal and abnormal) and three-level (Negative for Intraepithelial Lesion or Malignancy, Low-grade Squamous Intraepithelial Lesion and High-grade Squamous Intraepithelial Lesion) classes reflecting the established Bethesda system of classification used for diagnosis of cancerous or precancerous lesion of cervix. The system is evaluated on two generated databases obtained from two diagnostic centers, one containing 1610 single cervical cells and the other 1320 complete smear level images. The main objective of this database generation is to categorize the images according to the Bethesda system of classification both of which require lots of training and expertise. The system is also trained and tested on the benchmark Herlev University database which is publicly available. In this contribution a new segmentation technique has also been proposed for extracting shape features. Ripplet Type I transform, Histogram first order statistics and Gray Level Co-occurrence Matrix have been used for color and texture features respectively. To improve classification results, ensemble method is used, which integrates the decision of three classifiers. Assessments are performed using 5 fold cross validation. RESULTS Extended experiments reveal that the proposed system can successfully classify Pap smear images performing significantly better when compared with other existing methods. CONCLUSION This type of automated cancer classifier will be of particular help in early detection of cancer.
Interactive image retrieval with wavelet features Malay K. Kundu, Manish Chowdhury, Minakshi Banerjee Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2011
Method and system for quality assessment of objects in an industrial environment M Chowdhury, R Richardson WO Patent WO2023186316A1 , 2023 2023 Citations: 1
SceVar (Scenario Variations) Database: Real World Statistics driven Scenario Variations for AV Testing in Simulation: Abstraction of static and dynamic entities from road … MC Sagar Pathrudkar, Saikat Mukherjee, Vijaya Sarathi WebSci '21:13th ACM Web Science Conference 2021, 126-129 , 2021 2021
IHC-Net: A Fully Convolutional Neural Network for automated nuclear segmentation and ensemble classification for Allred scoring in breast pathology LB Mahanta, E hussain, N Das, L Kakoti, M Chowdhury Applied Soft Computing, https://doi.org/10.1016/j.asoc.2021.1071 , 2021 2021 Citations: 39
TTkkS The primary idea of our method is to borrow knowledge AS Charan, M Jitesh, M Chowdhury ELECTRONICS LETTERS 56 (24), 1309-1311 , 2020 2020
ABiFN: Attention-based bi-modal fusion network for object detection at night time A Sai Charan, M Jitesh, M Chowdhury, H Venkataraman Electronics Letters 56 (24), 1309-1311 , 2020 2020 Citations: 8
A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images E Hussain, LB Mahanta, CR Das, M Choudhury, M Chowdhury Artificial Intelligence in Medicine 101897 , 2020 2020 Citations: 98
Video shot boundary detection using multiscale geometric analysis of nsct and least squares support vector machine J Mondal, MK Kundu, S Das, M Chowdhury Multimedia Tools and Applications 77 (7), 8139-8161 , 2018 2018 Citations: 31
Automatic brain segmentation using artificial neural networks with shape context A Mahbod, M Chowdhury, Ö Smedby, C Wang Pattern Recognition Letters 101, 74-79 , 2018 2018 Citations: 48
Erratum to: Comparative assessment of efficiency for content based image retrieval systems using different wavelet features and pre-classifier M Chowdhury, MK Kundu Multimedia Tools and Applications 76 (20), 21839-21839 , 2017 2017 Citations: 1
Interactive radiographic image retrieval system MK Kundu, M Chowdhury, S Das Comput Methods Programs Biomed 139, 209-220 , 2017 2017 Citations: 12
Estimation of trabecular thickness in grayscale: An in vivo study M Platten, M Chowdhury, Ö Smedby, R Moreno European Society of Musculoskeletal Radiology , 2017 2017
Estimation of trabecular bone thickness in gray scale a validation study N Batool, M Chowdhury, R Moreno, Ö Smedby 31st International Congress and Exhibition on Computer Assisted Radiology … , 2017 2017
Segmentation of Cortical Bone using Fast Level Sets M Chowdhury, D Jörgens, W Chunliang, Ö Smedby, R Moreno SPIE Medical Imaging , 2017 2017 Citations: 1
Granulometry-Based Trabecular Bone Segmentation M Chowdhury, B Klintström, E Klintström, Ö Smedby, R Moreno Scandinavian Conference on Image Analysis , 2017 2017
Automated classification of Pap smear images to detect cervical dysplasia. K Bora, M Chowdhury, LB Mahanta, MK Kundu, AK Das Comput Methods Programs Biomed 138, 31--47 , 2017 2017 Citations: 242
An efficient radiographic image retrieval system using convolutional neural network M Chowdhury, SR Bulo, R Moreno, MK Kundu, Ö Smedby 2016 23rd international conference on pattern recognition (ICPR), 3134-3139 , 2016 2016 Citations: 20
Pap smear image classification using convolutional neural network AK Bora, Kangkana and Chowdhury, Manish and Mahanta, Lipi B. and Kundu ... Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and … , 2016 2016 Citations: 122
Prospects and problems of small tea growers in terai and Duars of West Bengal, India A Chowdhury, P Mandal, A Chowdhury, S Sarkar, M Chowdhury International Journal of Current Advanced Research 5 (2), 587-590 , 2016 2016 Citations: 15
Comparative assessment of efficiency for content based image retrieval systems using different wavelet features and pre-classifier M Chowdhury, MK Kundu Multimedia Tools and Applications 74 (24), 11595-11630 , 2015 2015 Citations: 15
Endoscopic Image Retrieval System Using Multi-scale Image Features M Chowdhury, MK Kundu Proceedings of the 2nd International Conference on Perception and Machine … , 2015 2015 Citations: 5
MOST CITED SCHOLAR PUBLICATIONS
Automated classification of Pap smear images to detect cervical dysplasia. K Bora, M Chowdhury, LB Mahanta, MK Kundu, AK Das Comput Methods Programs Biomed 138, 31--47 , 2017 2017 Citations: 242
Brain MR image classification using multiscale geometric analysis of ripplet S Das, M Chowdhury, MK Kundu Progress In Electromagnetics Research 137, 1-17 , 2013 2013 Citations: 156
Pap smear image classification using convolutional neural network AK Bora, Kangkana and Chowdhury, Manish and Mahanta, Lipi B. and Kundu ... Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and … , 2016 2016 Citations: 122
A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images E Hussain, LB Mahanta, CR Das, M Choudhury, M Chowdhury Artificial Intelligence in Medicine 101897 , 2020 2020 Citations: 98
A graph-based relevance feedback mechanism in content-based image retrieval MK Kundu, M Chowdhury, SR Bulo Knowledge-Based Systems 73, 254-264 , 2015 2015 Citations: 90
Medical image fusion based on ripplet transform type-I MK KUNDU, M Chowdhury Progress In Electromagnetics Research B , 2011 2011 Citations: 89
Automatic brain segmentation using artificial neural networks with shape context A Mahbod, M Chowdhury, Ö Smedby, C Wang Pattern Recognition Letters 101, 74-79 , 2018 2018 Citations: 48
IHC-Net: A Fully Convolutional Neural Network for automated nuclear segmentation and ensemble classification for Allred scoring in breast pathology LB Mahanta, E hussain, N Das, L Kakoti, M Chowdhury Applied Soft Computing, https://doi.org/10.1016/j.asoc.2021.1071 , 2021 2021 Citations: 39
Video shot boundary detection using multiscale geometric analysis of nsct and least squares support vector machine J Mondal, MK Kundu, S Das, M Chowdhury Multimedia Tools and Applications 77 (7), 8139-8161 , 2018 2018 Citations: 31
Interactive content based image retrieval using ripplet transform and fuzzy relevance feedback M Chowdhury, S Das, MK Kundu Indo-Japanese Conference on Perception and Machine Intelligence, 243-251 , 2012 2012 Citations: 24
Novel CBIR system based on ripplet transform using interactive neuro-fuzzy technique M Chowdhury, S Das, MK Kundu ELCVIA: electronic letters on computer vision and image analysis, 1-13 , 2012 2012 Citations: 23
An efficient radiographic image retrieval system using convolutional neural network M Chowdhury, SR Bulo, R Moreno, MK Kundu, Ö Smedby 2016 23rd international conference on pattern recognition (ICPR), 3134-3139 , 2016 2016 Citations: 20
Prospects and problems of small tea growers in terai and Duars of West Bengal, India A Chowdhury, P Mandal, A Chowdhury, S Sarkar, M Chowdhury International Journal of Current Advanced Research 5 (2), 587-590 , 2016 2016 Citations: 15
Comparative assessment of efficiency for content based image retrieval systems using different wavelet features and pre-classifier M Chowdhury, MK Kundu Multimedia Tools and Applications 74 (24), 11595-11630 , 2015 2015 Citations: 15
Interactive radiographic image retrieval system MK Kundu, M Chowdhury, S Das Comput Methods Programs Biomed 139, 209-220 , 2017 2017 Citations: 12
Interactive image retrieval using M-band wavelet, earth mover’s distance and fuzzy relevance feedback MK Kundu, M Chowdhury, M Banerjee International Journal of Machine Learning and Cybernetics 3 (4), 285-296 , 2012 2012 Citations: 12
Effective Classification of Radiographic Medical Images Using LS-SVM and NSCT based Retrieval System M Chowdhury, S Das, MK Kundu CODEC 2012 1, 1 , 2012 2012 Citations: 11
Compact Image Signature Generation: An Application in Image Retrieval M Chowdhury, S Das, MK Kundu 5th International Conference on Computer Science and Information Technology … , 2013 2013 Citations: 9
ABiFN: Attention-based bi-modal fusion network for object detection at night time A Sai Charan, M Jitesh, M Chowdhury, H Venkataraman Electronics Letters 56 (24), 1309-1311 , 2020 2020 Citations: 8
Interactive image retrieval with wavelet features MK Kundu, M Chowdhury, M Banerjee International Conference on Pattern Recognition and Machine Intelligence … , 2011 2011 Citations: 8
