Mrinal Mandal

University of Alberta, Edmonton, Alberta, Canada

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Publications (23)27.03 Total impact

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    Hongming Xu · Mrinal Mandal
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    ABSTRACT: Segmentation of epidermis areas is an important step towards automatic analysis of skin histopathological images. This paper presents a robust technique for epidermis segmentation in whole slide skin histopathological images. The proposed technique first performs a coarse epidermis segmentation using global thresholding and shape analysis. The epidermis thickness is then estimated by a series of line segments perpendicular to the main axis of the initially segmented epidermis mask. If the segmented epidermis mask has a thickness greater than a predefined threshold, the segmentation is suspected to be inaccurate. A second pass of fine segmentation using k-means algorithm is then carried out over these coarsely segmented result to enhance the performance. Experimental results on 64 different skin histopathological images show that the proposed technique provides a superior performance compared to the existing techniques.
    Full-text · Conference Paper · Aug 2015
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    Hongming Xu · Cheng Lu · Mrinal Mandal
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    ABSTRACT: In the diagnosis of skin melanoma by analyzing histopathological images, the epidermis and epidermis-dermis junctional areas are regions of interest as they provide the most important histologic diagnosis features. This paper presents an automated technique for segmenting epidermis and dermis regions from whole slide skin histopathological images. The proposed technique first performs epidermis segmentation using a thresholding and thickness measurement based method. The dermis area is then segmented based on a predefined depth of segmentation from the epidermis outer boundary. Experimental results on 66 different skin images show that the proposed technique can robustly segment regions of interest as desired.
    Full-text · Conference Paper · Aug 2015
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    Cheng Lu · Mrinal Mandal · Zhen Ma
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    ABSTRACT: With the development of high-speed, high-resolution whole slide histology digital scanners, glass slides of tissue specimen can now be digitised at high magnification to create the whole slide image. Quantitative image analysis tools are then desirable to help the pathologist for their routine examination. Epidermis area is a very important observation area for the cancer diagnosis. Therefore, in order to build up a computer-aided diagnosis system, segmentation of the epidermis area is often the very first and crucial step. An improved computer-aided epidermis segmentation technique for the whole slide skin histopathological image is proposed in this study. The proposed technique first obtains an initial segmentation result with the help of global thresholding and shape analysis. A template matching method, with adaptive template intensity value, is then applied. Finally, a threshold is calculated based on the probability density function of the response value image. Experimental results show that the proposed technique overcomes the limitation of the existing technique and provides superior performance, with sensitivity of 95.68%, specificity of 99.41% and precision of 93.13%. The performance of the proposed technique is satisfactory for future clinical use.
    Full-text · Article · Jun 2015 · IET Image Processing
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    Cheng Lu · Mrinal Mandal
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    ABSTRACT: Melanoma is the most aggressive type of skin cancer, and the pathological examination remains the gold standard for the final diagnosis. Traditionally, the histopathology slides are examined under a microscope by pathologists which typically leads to inter-and intra-observer variations. In addition, it is time consuming and tedious to analyze a whole glass slide manually. In this paper, we propose an efficient technique for automated analysis and diagnosis of the skin whole slide image. The proposed technique consists of five modules: epidermis segmentation, keratinocytes segmentation, melanocytes detection, feature construction and classification. Since the epidermis, keratinocytes and melanocytes are important cues for the pathologists, these regions are first segmented. Based on the segmented regions of interest, the spatial distribution and morphological features are constructed. These features, representing a skin tissue, are classified by a multi-class support vector machine classifier. Experimental results show that the proposed technique is able to provide a satisfactory performance (with about 90% classification accuracy) and is able to assist the pathologist for the skin tissue analysis and diagnosis. &
    Full-text · Article · May 2015 · Pattern Recognition
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    Cheng Lu · Mengyao Ji · Zhen Ma · Mrinal Mandal
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    ABSTRACT: We developed a computer-aided technique to study nuclear atypia classification in high-power field haematoxylin and eosin stained images. An automated technique for nuclear atypia score (NAS) calculation is proposed. The proposed technique uses sophisticated digital image analysis and machine-learning methods to measure the NAS for haematoxylin and eosin stained images. The proposed technique first segments all nuclei regions. A set of morphology and texture features is extracted from presegmented nuclei regions. The histogram of each feature is then calculated to characterize the statistical information of the nuclei. Finally, a support vector machine classifier is applied to classify a high-power field image into different nuclear atypia classes. A set of 1188 digital images was analysed in the experiment. We successfully differentiated the high-power field image with NAS1 versus non-NAS1, NAS2 versus non-NAS2 and NAS3 versus non-NAS3, with area under receiver-operating characteristic curve of 0.90, 0.86 and 0.87, respectively. In three classes evaluation, the average classification accuracy was 78.79%. We found that texture-based feature provides best performance for the classification. The automated technique is able to quantify statistical features that may be difficult to be measured by human and demonstrates the future potentials of automated image analysis technique in histopathology analysis. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.
    Full-text · Article · Mar 2015 · Journal of Microscopy
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    Hongming Xu · Cheng Lu · Mrinal Mandal
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    ABSTRACT: In this paper, we propose an efficient method for segmenting cell nuclei in the skin histopathological images. The proposed technique consists of four modules. First it separates the nuclei regions from the background with an adaptive threshold technique. Next an elliptical descriptor is used to detect the isolated nuclei with elliptical shapes. This descriptor classifies the nuclei regions based on two ellipticity parameters. Nuclei clumps and nuclei with irregular shapes are then localized by an improved seed detection technique based on voting in the eroded nuclei regions. Finally, undivided nuclei regions are segmented by a marked watershed algorithm. Experimental results on 114 different image patches indicate that the proposed technique provides a superior performance in nuclei detection and segmentation.
    Full-text · Article · Sep 2014 · IEEE Journal of Biomedical and Health Informatics
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    Cheng Lu · Mrinal Mandal
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    ABSTRACT: In order to develop a computer-aided diagnosis system for histopathological skin cancer diagnosis, segmentation of the epidermis area is the very first and crucial step. An improved computer-aided epidermis segmentation technique for the whole slide skin histopathological image is proposed in this paper. The proposed technique first obtains an initial segmentation result with the help of global thresholding and shape analysis. A template matching method, with adaptive template intensity value, is then applied. Finally, a threshold is calculated based on the probability density function of the processed image after template matching. The threshold is then used to obtain the final segmentation result. Experimental results show that the proposed technique overcomes the limitation of the existing technique and provides a superior performance with sensitivity at 97.99%, and precision at 96.00%.
    Full-text · Article · Aug 2014
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    Cheng Lu · Mrinal Mandal
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    ABSTRACT: The count of mitotic cells is a critical factor in most cancer grading systems. Extracting the mitotic cell from the histopathological image is a very challenging task. In this paper, we propose an efficient technique for detecting and segmenting the mitotic cells in the high-resolution multispectral image. The proposed technique consists of three main modules: discriminative image generation, mitotic cell candidate detection and segmentation, and mitotic cell candidate classification. In the first module, a discriminative image is obtained by linear discriminant analysis using ten different spectral band images. A set of mitotic cell candidate regions is then detected and segmented by the Bayesian modeling and local-region threshold method. In the third module, a 226 dimension feature is extracted from the mitotic cell candidates and their surrounding regions. An imbalanced classification framework is then applied to perform the classification for the mitotic cell candidates in order to detect the real mitotic cells. The proposed technique has been evaluated on a publicly available dataset of 35 $times$ 10 multispectral images, in which 224 mitotic cells are manually labeled by experts. The proposed technique is able to provide superior performance compared to the existing technique, 81.5% sensitivity rate and 33.9% precision rate in terms of detection performance, and 89.3% sensitivity rate and 87.5% precision rate in terms of segmentation performance.
    Full-text · Article · Mar 2014 · IEEE Journal of Biomedical and Health Informatics
  • Yanan Fu · Wei Zhang · Mrinal Mandal · Max Q.-H Meng
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    ABSTRACT: Wireless capsule endoscopy (WCE) can directly take digital images in the gastrointestinal tract of a patient. It has opened a new chapter in small intestine examination. However, a major problem associated with this technology is that too many images need to be manually examined by clinicians. Currently, there is no standard for capsule endoscopy image interpretation and classification. Most state-of-the-art CAD methods often suffer from poor performance, high computational cost, or multiple empirical thresholds. In this paper, a new method for rapid bleeding detection in the WCE video is proposed. We group pixels through superpixel segmentation to reduce the computational complexity while maintaining high diagnostic accuracy. Feature of each superpixel is extracted using the red ratio in RGB space and fed into support vector machine for classification. Also, the influence of edge pixels has been removed in this paper. Comparative experiments show that our algorithm is superior to the existing methods in terms of sensitivity, specificity, and accuracy.
    No preview · Article · Mar 2014 · IEEE Journal of Biomedical and Health Informatics
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    Yue Li · Mrinal Mandal · Cheng Lu
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    ABSTRACT: Detection of singular points (SPs) in fingerprint images is an important task in fingerprint recognition. In this paper, we propose a novel technique for SPs detection using orientation field regularization and the Poincaré Index (PI) technique. The squared orientation field is first extracted from a fingerprint image. In order to distinguish the local orientation patterns of genuine SPs from that of spurious SPs, a novel technique based on the Discrete Hodge Helmholtz Decomposition (DHHD) is proposed to reconstruct a regular orientation field of the fingerprint. Based on the regular orientation field, the PI technique is then applied to extract the SPs. Experimental results on the public fingerprint database FVC2002 show that, the proposed technique is rather accurate and robust in identifying SPs.
    Full-text · Conference Paper · Oct 2013
  • Yue Li · Mrinal Mandal · S Nizam Ahmed
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    ABSTRACT: In the diagnosis of various brain disorders by analyzing the brain magnetic resonance images (MRI), the segmentation of corpus callosum (CC) is a crucial step. In this paper, we propose a fully automated technique for CC segmentation in the T1-weighted midsagittal brain MRIs. An adaptive mean shift clustering technique is first used to cluster homogenous regions in the image. In order to distinguish the CC from other brain tissues, area analysis, template matching, in conjunction with the shape and location analysis are proposed to identify the CC area. The boundary of detected CC area is then used as the initial contour in the Geometric Active Contour (GAC) model, and evolved to get the final segmentation result. Experimental results demonstrate that the proposed technique overcomes the problem of manual initialization in existing GAC technique, and provides a reliable segmentation performance.
    No preview · Article · Jul 2013 · Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
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    Cheng Lu · Muhammad Mahmood · Naresh Jha · Mrinal Mandal
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    ABSTRACT: In the diagnosis of skin melanoma by analyzing histopathological images, the detection of the melanocytes in the epidermis area is an important step. However, the detection of the melanocytes from the epidermis area is difficult because other keratinocytes that are very similar to the melanocytes are also present. This paper proposes a novel computer-aided technique for detection of the melanocytes in the epidermis area of skin histopathological images. An adaptive threshold technique is first applied to segment all the keratinocytes in the image. In order to distinguish the melanocytes from other keratinocytes, a novel technique based on radial line scanning is proposed to estimate the halo region of the melanocytes. Based on the estimated halo region of all the nuclei, an area ratio of estimated halo region and the nuclei is used to detect the melanocytes from all the keratinocytes. Experimental results on 40 different histopathological images of skin tissue containing 341 melanocytes show that the proposed technique provides a superior performance.
    Full-text · Article · Feb 2013 · Pattern Recognition
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    Cheng Lu · Mrinal Mandal
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    ABSTRACT: In this paper, we propose a robust technique for temporal alignment of video sequences with similar planar motions acquired using uncalibrated cameras. In this technique, we model the motion-based video temporal alignment problem as a spatio-temporal discrete trajectory point sets alignment problem. First, the trajectory of the object of interest is tracked throughout the videos. A probabilistic method is then developed to calculate the `soft' spatial correspondence between the trajectory point sets. Next, a dynamic time warping technique (DTW) is applied to the spatial correspondence information to compute the temporal alignment of the videos. The experimental results show that the proposed technique provides a superior performance over existing techniques for videos with similar trajectory patterns.
    Full-text · Article · Jan 2013 · IEEE Transactions on Multimedia
  • Yanan Fu · Mrinal Mandal · David W. Zhang · Max Q.-h. Meng
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    ABSTRACT: Wireless capsule endoscopy (WCE) is an imaging technology that enables close examination of the interior of the entire small intestine. A major problem associated with this new technology is that a large volume of video data need to be examined manually by clinicians. It is therefore useful to design a mechanism that allows the clinicians to gain certain evaluation of a video without watching the whole video. In this paper, a shot detection-based method is presented for automatically establishing the WCE video static storyboard, and then moving storyboard is extracted based on the selected representative frames under the supervision of clinicians. Experimental results show that most of the representative frames containing relevant features can be extracted from the original WCE video. The proposed method can significantly and safely reduce the number of frames that need to be examined by clinicians and thus speed up the diagnosis procedures.
    No preview · Article · Dec 2012 · International Journal of Information Acquisition
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    Cheng Lu · Muhammad Mahmood · Naresh Jha · Mrinal Mandal
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    ABSTRACT: To develop a computer-aided robust nuclei segmentation technique for quantitative histopathological image analysis. A robust nuclei segmentation technique for histopathological image analysis is proposed. The proposed technique uses a hybrid morphological reconstruction module to reduce the intensity variation within the nuclei regions and suppress the noise in the image. A local region adaptive threshold selection module based on local optimal threshold is used to segment the nuclei. The technique incorporates domain-specific knowledge of skin histopathological images for more accurate segmentation results. The technique is compared to the manually labeled nuclei locations and nuclei boundaries for the performance evaluations. On different histopathological images of skin epidermis with complex background, containing more than 3000 nuclei, the technique provides a good nuclei detection performance: 88.11% sensitivity rate, 80.02% positive prediction rate and only 5.34% under-segmentation rate compared to the manually labeled nuclei locations. Compared to the 110 manually segmented nuclei regions, the proposed technique provides a good segmentation performance (in terms of the nucleus area, perimeter, and form factor). The proposed technique is able to provide more accurate segmentation performance compared to the existing techniques and can be employed for quantitative analysis of the histopathological images.
    Full-text · Article · Dec 2012 · Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology
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    Cheng Lu · Mrinal Mandal
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    ABSTRACT: In the diagnosis of skin melanoma by analyzing histopathological images, the segmentation of the epidermis area is an important step. This paper proposes a computer-aided technique for segmentation and analysis of the epidermis area in the whole slide skin histopathological images. Before the segmentation technique is employed, a monochromatic color channel that provides a good discriminant information between the epidermis and dermis areas is determined. In order to reduce the processing time and perform the analysis efficiently, we employ multi-resolution image analysis in the proposed segmentation technique. At first, a low resolution whole slide image is generated. We then segment the low resolution image using a global threshold method and shape analysis. Based on the segmented epidermis area, the layout of epidermis is determined and the high resolution image tiles of epidermis are generated for further manual or automated analysis. Experimental results on 16 different whole slide skin images show that the proposed technique provides a superior performance, about 92% sensitivity rate, 93% precision and 97% specificity rate.
    Full-text · Article · Aug 2012 · Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
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    ABSTRACT: The 14 papers in this special issue are extended versions of papers presented at ICME 2011, held in Barcelona, Spain, on 11-15 July 2011.
    Full-text · Article · Jun 2012 · IEEE Transactions on Multimedia
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    Cheng Lu · Muhammad Mahmood · Naresh Jha · Mrinal Mandal
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    ABSTRACT: In the diagnosis of skin melanoma by analyzing histopathological images, the detection of the melanocytes in the epidermis area is an important step. However, the detection of melanocytes in the epidermis area is dicult because other keratinocytes that are very similar to the melanocytes are also present. This paper proposes a novel computer-aided technique for segmentation of the melanocytes in the skin histopathological images. In order to reduce the local intensity variant, a mean-shift algorithm is applied for the initial segmentation of the image. A local region recursive segmentation algorithm is then proposed to filter out the candidate nuclei regions based on the domain prior knowledge. To distinguish the melanocytes from other keratinocytes in the epidermis area, a novel descriptor, named local double ellipse descriptor (LDED), is proposed to measure the local features of the candidate regions. The LDED uses two parameters: region ellipticity and local pattern characteristics to distinguish the melanocytes from the candidate nuclei regions. Experimental results on 28 dierent histopathological images of skin tissue with dierent zooming factors show that the proposed technique provides a superior performance.
    Full-text · Article · May 2012 · IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society
  • Amit Phadikar · Santi P. Maity · Mrinal Mandal
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    ABSTRACT: This paper proposes a tamper detection and correction technique using semi-fragile data hiding that aims to achieve high perceptual quality of images at the user-end even after malicious modifications. A binary signature and an image digest are embedded by modulating integer wavelet coefficients using dither modulation based quantization index modulation. Half-toning technique is used to obtain image digest from the low-resolution version of the host image itself. Decoder extracts the binary signature from the watermarked image for tamper detection, while the extracted image digest is used to correct the tamper region. Unlike previously proposed techniques, this novel approach distinguishes malicious changes from various common image processing operations more efficiently and also correct tapered regions effectively. Experimental results show that the proposed technique provides a superior performance in terms of probability of miss and false alarm as well as in tamper correction, compared to several existing semi-fragile watermarking techniques.
    No preview · Article · Apr 2012 · Journal of Visual Communication and Image Representation
  • Amit Phadikar · Santi Prasad Maity · Mrinal Mandal
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    ABSTRACT: This paper proposes a tamper detection and correction scheme using semi-fragile data hiding that aims to achieve high perceptual quality of images at the end-user even after malicious modification. The objective is achieved by embedding an external binary signature as well a low-resolution version of the image (image digest) by modulating integer wavelet coefficients using quantization index modulation (QIM). Half-toning technique is used to obtain image digest from the low-resolution version of the image. The receiver extracts the binary signature from the watermarked image for tamper detection while the extracted image digest is used to correct the tampered region. Unlike previously proposed techniques, this novel approach distinguishes malicious changes from various common image processing operations more efficiently and also correct tapered regions effectively. We compare the performance of the proposed method in term of probability of miss and false alarm with the same for the some of the existing semi-fragile watermarking techniques to demonstrate the success and potential of the present one.
    No preview · Conference Paper · May 2010