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Chowdhury, Manish
Publications (10 of 11) Show all publications
Hussain, E., Mahanta, L. B., Das, C. R., Choudhury, M. & Chowdhury, M. (2020). A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images. Artificial Intelligence in Medicine, 107, Article ID 101897.
Open this publication in new window or tab >>A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images
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2020 (English)In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 107, article id 101897Article in journal (Refereed) Published
Abstract [en]

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 chal-lenging 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 The number of convolutional layers in the standard Unet has been replaced by densely connected blocks to ensure feature reuse-ability property while the introduction of residual blocks in the same attempts to converge the network more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 simi-larity 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.

Place, publisher, year, edition, pages
Elsevier BV, 2020
Keywords
Liquid-based cytology, Pap smear, Fully convolutional neural network, Segmentation, Classification
National Category
Medical Image Processing
Identifiers
urn:nbn:se:kth:diva-282290 (URN)10.1016/j.artmed.2020.101897 (DOI)000566856900006 ()32828445 (PubMedID)2-s2.0-85086079448 (Scopus ID)
Note

QC 20201007

Available from: 2020-10-07 Created: 2020-10-07 Last updated: 2022-06-25Bibliographically approved
Mahbod, A., Chowdhury, M., Smedby, Ö. & Wang, C. (2018). Automatic brain segmentation using artificial neural networks with shape context. Pattern Recognition Letters, 101, 74-79
Open this publication in new window or tab >>Automatic brain segmentation using artificial neural networks with shape context
2018 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 101, p. 74-79Article in journal (Refereed) Published
Abstract [en]

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 have been proposed in the literature in order to reduce the requirement of user intervention, but the level of accuracy in most cases is still inferior to that of manual segmentation. We propose a new brain segmentation method that integrates volumetric shape models into a supervised artificial neural network (ANN) framework. This is done by running a preliminary level-set based statistical shape fitting process guided by the image intensity and then passing the signed distance maps of several key structures to the ANN as feature channels, in addition to the conventional spatial-based and intensity-based image features. The so-called shape context information is expected to help the ANN to learn local adaptive classification rules instead of applying universal rules directly on the local appearance features. The proposed method was tested on a public datasets available within the open MICCAI grand challenge (MRBrainS13). The obtained average Dice coefficient were 84.78%, 88.47%, 82.76%, 95.37% and 97.73% for gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), brain (WM + GM) and intracranial volume respectively. Compared with other methods tested on the same dataset, the proposed method achieved competitive results with comparatively shorter training time.

Place, publisher, year, edition, pages
Elsevier, 2018
National Category
Medical Image Processing
Identifiers
urn:nbn:se:kth:diva-219889 (URN)10.1016/j.patrec.2017.11.016 (DOI)000418101400011 ()2-s2.0-85036471005 (Scopus ID)
Note

QC 20171215

Available from: 2017-12-15 Created: 2017-12-15 Last updated: 2022-06-26Bibliographically approved
Mondal, J., Kundu, M. K., Das, S. & Chowdhury, M. (2018). Video shot boundary detection using multiscale geometric analysis of nsct and least squares support vector machine. Multimedia tools and applications, 77(7), 8139-8161
Open this publication in new window or tab >>Video shot boundary detection using multiscale geometric analysis of nsct and least squares support vector machine
2018 (English)In: Multimedia tools and applications, ISSN 1380-7501, E-ISSN 1573-7721, Vol. 77, no 7, p. 8139-8161Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
SPRINGER, 2018
Keywords
Shot boundary detection, Abrupt transition, Gradual transition, Principal component analysis, Non-subsampled contourlet transform, Least squares support vector machine
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-227231 (URN)10.1007/s11042-017-4707-9 (DOI)000429355800017 ()2-s2.0-85018717551 (Scopus ID)
Note

QC 20180518

Available from: 2018-05-18 Created: 2018-05-18 Last updated: 2022-06-26Bibliographically approved
Bora, K., Chowdhury, M., Mahanta, L. B., Kundu, M. K. & Das, A. K. (2017). Automated classification of Pap smear images to detect cervical dysplasia. Computer Methods and Programs in Biomedicine, 138, 31-47
Open this publication in new window or tab >>Automated classification of Pap smear images to detect cervical dysplasia
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2017 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 138, p. 31-47Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Pap smear, MSER, Ripplet transform, Ensemble classification
National Category
Biomedical Laboratory Science/Technology
Identifiers
urn:nbn:se:kth:diva-199468 (URN)10.1016/j.cmpb.2016.10.001 (DOI)000389285300005 ()27886713 (PubMedID)2-s2.0-84994045082 (Scopus ID)
Note

QC 20170123

Available from: 2017-01-23 Created: 2017-01-09 Last updated: 2024-03-18Bibliographically approved
Batool, N., Chowdhury, M., Smedby, Ö. & Moreno, R. (2017). Estimation of trabecular bone thickness in gray scale: a validation study. International Journal of Computer Assisted Radiology and Surgery, 12(Supplement 1)
Open this publication in new window or tab >>Estimation of trabecular bone thickness in gray scale: a validation study
2017 (English)In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, Vol. 12, no Supplement 1Article in journal, Meeting abstract (Refereed) Published
National Category
Medical Image Processing
Identifiers
urn:nbn:se:kth:diva-259764 (URN)
Note

QC 20220412

Available from: 2019-09-23 Created: 2019-09-23 Last updated: 2022-06-26Bibliographically approved
Platten, M., Chowdhury, M., Smedby, Ö. & Moreno, R. (2017). Estimation of trabecular thickness in grayscale: an in vivo study. In: ESSR 2017 / P-0196: . Paper presented at European Society of Musculoskeletal Radiology, Annual Scientific Meeting, ESSR 2017.
Open this publication in new window or tab >>Estimation of trabecular thickness in grayscale: an in vivo study
2017 (English)In: ESSR 2017 / P-0196, 2017Conference paper, Poster (with or without abstract) (Refereed)
National Category
Medical Image Processing
Identifiers
urn:nbn:se:kth:diva-259763 (URN)10.1594/essr2017/P-0196 (DOI)
Conference
European Society of Musculoskeletal Radiology, Annual Scientific Meeting, ESSR 2017
Note

QC 20191011

Available from: 2019-09-23 Created: 2019-09-23 Last updated: 2022-12-05Bibliographically approved
Chowdhury, M., Klintström, B., Klintström, E., Smedby, Ö. & Moreno, R. (2017). Granulometry-based trabecular bone segmentation. In: 20th Scandinavian Conference on Image Analysis, SCIA 2017: . Paper presented at 20th Scandinavian Conference on Image Analysis, SCIA 2017, Tromso, Norway, 12 June 2017 through 14 June 2017 (pp. 100-108). Springer, 10270
Open this publication in new window or tab >>Granulometry-based trabecular bone segmentation
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2017 (English)In: 20th Scandinavian Conference on Image Analysis, SCIA 2017, Springer, 2017, Vol. 10270, p. 100-108Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10270
Keywords
Cone beam computed tomography, Granulometry, Segmentation, Trabecular bone
National Category
Medical Image Processing
Identifiers
urn:nbn:se:kth:diva-210015 (URN)10.1007/978-3-319-59129-2_9 (DOI)000454360300009 ()2-s2.0-85020456098 (Scopus ID)9783319591285 (ISBN)
Conference
20th Scandinavian Conference on Image Analysis, SCIA 2017, Tromso, Norway, 12 June 2017 through 14 June 2017
Note

QC 20170627

Available from: 2017-06-27 Created: 2017-06-27 Last updated: 2024-03-18Bibliographically approved
Kundu, M. K., Chowdhury, M. & Das, S. (2017). Interactive radiographic image retrieval system. Computer Methods and Programs in Biomedicine, 139, 209-220
Open this publication in new window or tab >>Interactive radiographic image retrieval system
2017 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 139, p. 209-220Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Content based medical image retrieval, Fuzzy logic, Multiscale geometric analysis, Pulse couple neural network, Radiographic images, Relevance feedback, Classification (of information), Complex networks, Content based retrieval, Database systems, Diagnosis, Feedback control, Hierarchical systems, Image enhancement, Image retrieval, Information retrieval, Medical applications, Medical imaging, Medical information systems, Neural networks, Semantics, Vector spaces, Image retrieval in medical applications, Multi-scale geometric analysis, Non-sub-sampled contourlet transforms, Subjectivity of human perception, Unsupervised feature selection, Search engines, classifier, comparative effectiveness, compression, data base, experimental model, feedback system, human, nervous system, perception, quantitative study, visual information
National Category
Medical Engineering
Identifiers
urn:nbn:se:kth:diva-202209 (URN)10.1016/j.cmpb.2016.10.023 (DOI)000395223200019 ()28187892 (PubMedID)2-s2.0-85007093170 (Scopus ID)
Note

Correspondence Address: Chowdhury, M.; Machine Intelligence Unit, Indian Statistical InstituteIndia; email: st.manishc@gmail.com. QC 20170320

Available from: 2017-03-20 Created: 2017-03-20 Last updated: 2024-03-18Bibliographically approved
Chowdhury, M., Jörgens, D., Wang, C., Smedby, Ö. & Moreno, R. (2017). Segmentation of Cortical Bone using Fast Level Sets. In: Styner, MA Angelini, ED (Ed.), MEDICAL IMAGING 2017: IMAGE PROCESSING. Paper presented at Conference on Medical Imaging - Image Processing, FEB 12-14, 2017, Orlando, FL. SPIE - International Society for Optical Engineering, Article ID UNSP 1013327.
Open this publication in new window or tab >>Segmentation of Cortical Bone using Fast Level Sets
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2017 (English)In: MEDICAL IMAGING 2017: IMAGE PROCESSING / [ed] Styner, MA Angelini, ED, SPIE - International Society for Optical Engineering, 2017, article id UNSP 1013327Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2017
Series
Proceedings of SPIE, ISSN 0277-786X ; 10133
National Category
Medical Image Processing
Identifiers
urn:nbn:se:kth:diva-211764 (URN)10.1117/12.2254240 (DOI)000405564600075 ()2-s2.0-85020302337 (Scopus ID)978-1-5106-0711-8 (ISBN)
Conference
Conference on Medical Imaging - Image Processing, FEB 12-14, 2017, Orlando, FL
Note

QC 20170811

Available from: 2017-08-11 Created: 2017-08-11 Last updated: 2024-03-18Bibliographically approved
Chowdhury, M., Rota Bulò, S., Moreno, R., Kundu, M. & Smedby, Ö. (2016). An Efficient Radiographic Image Retrieval System Using Convolutional Neural Network. In: 2016 23rd International Conference on Pattern Recognition (ICPR): . Paper presented at 23rd International Conference on Pattern Recognition, ICPR 2016, Cancun CenterCancun, Mexico, 4 December 2016 through 8 December 2016 (pp. 3134-3139). Institute of Electrical and Electronics Engineers (IEEE), Article ID 7900116.
Open this publication in new window or tab >>An Efficient Radiographic Image Retrieval System Using Convolutional Neural Network
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2016 (English)In: 2016 23rd International Conference on Pattern Recognition (ICPR), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 3134-3139, article id 7900116Conference paper, Published paper (Refereed)
Abstract [en]

Content-Based Medical Image Retrieval (CBMIR) is an important research field in the context of medical data management. In this paper we propose a novel CBMIR system for the automatic retrieval of radiographic images. Our approach employs a Convolutional Neural Network (CNN) to obtain high- level image representations that enable a coarse retrieval of images that are in correspondence to a query image. The retrieved set of images is refined via a non-parametric estimation of putative classes for the query image, which are used to filter out potential outliers in favour of more relevant images belonging to those classes. The refined set of images is finally re-ranked using Edge Histogram Descriptor, i.e. a low-level edge-based image descriptor that allows to capture finer similarities between the retrieved set of images and the query image. To improve the computational efficiency of the system, we employ dimensionality reduction via Principal Component Analysis (PCA). Experiments were carried out to evaluate the effectiveness of the proposed system on medical data from the “Image Retrieval in Medical Applications” (IRMA) benchmark database. The obtained results show the effectiveness of the proposed CBMIR system in the field of medical image retrieval.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Series
Proceedings - International Conference on Pattern Recognition, ISSN 1051-4651
Keywords
Content based image retrieval and data mining, Medical image and signal analysis, Deep learning
National Category
Medical Image Processing
Identifiers
urn:nbn:se:kth:diva-197570 (URN)10.1109/ICPR.2016.7900116 (DOI)000406771303020 ()2-s2.0-85019074329 (Scopus ID)9781509048472 (ISBN)
Conference
23rd International Conference on Pattern Recognition, ICPR 2016, Cancun CenterCancun, Mexico, 4 December 2016 through 8 December 2016
Funder
Swedish Research Council, 2012-3512Swedish Research Council, 2014-6153VINNOVA, E9126
Note

QC 20161208

Available from: 2016-12-05 Created: 2016-12-05 Last updated: 2024-03-18Bibliographically approved
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