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  • 1. Abu-Rmileh, Amjad
    et al.
    Garcia-Gabin, Winston
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Wiener sliding-mode control for artificial pancreas: A new nonlinear approach to glucose regulation2012In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, ISSN 0169-2607, Vol. 107, no 2, p. 327-340Article in journal (Refereed)
    Abstract [en]

    Type 1 diabetic patients need insulin therapy to keep their blood glucose close to normal. In this paper an attempt is made to show how nonlinear control-oriented model may be used to improve the performance of closed-loop control of blood glucose in diabetic patients. The nonlinear Wiener model is used as a novel modeling approach to be applied to the glucose control problem. The identified Wiener model is used in the design of a robust nonlinear sliding mode control strategy. Two configurations of the nonlinear controller are tested and compared to a controller designed with a linear model. The controllers are designed in a Smith predictor structure to reduce the effect of system time delay. To improve the meal compensation features, the controllers are provided with a simple feedforward controller to inject an insulin bolus at meal time. Different simulation scenarios have been used to evaluate the proposed controllers. The obtained results show that the new approach out-performs the linear control scheme, and regulates the glucose level within safe limits in the presence of measurement and modeling errors, meal uncertainty and patient variations.

  • 2. Bora, Kangkana
    et al.
    Chowdhury, Manish
    KTH, School of Technology and Health (STH).
    Mahanta, Lipi B.
    Kundu, Malay Kumar
    Das, Anup Kumar
    Automated classification of Pap smear images to detect cervical dysplasia2017In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 138, p. 31-47Article in journal (Refereed)
    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.

  • 3. Kundu, M. K.
    et al.
    Chowdhury, Manish
    KTH, School of Technology and Health (STH).
    Das, S.
    Interactive radiographic image retrieval system2017In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 139, p. 209-220Article in journal (Refereed)
    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.

  • 4. Mahbod, A.
    et al.
    Schaefer, G.
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
    Dorffner, G.
    Ecker, R.
    Ellinger, I.
    Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification2020In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 193, p. 105475-, article id 105475Article in journal (Refereed)
    Abstract [en]

    Background and objective: Skin cancer is among the most common cancer types in the white population and consequently computer aided methods for skin lesion classification based on dermoscopic images are of great interest. A promising approach for this uses transfer learning to adapt pre-trained convolutional neural networks (CNNs) for skin lesion diagnosis. Since pre-training commonly occurs with natural images of a fixed image resolution and these training images are usually significantly smaller than dermoscopic images, downsampling or cropping of skin lesion images is required. This however may result in a loss of useful medical information, while the ideal resizing or cropping factor of dermoscopic images for the fine-tuning process remains unknown. Methods: We investigate the effect of image size for skin lesion classification based on pre-trained CNNs and transfer learning. Dermoscopic images from the International Skin Imaging Collaboration (ISIC) skin lesion classification challenge datasets are either resized to or cropped at six different sizes ranging from 224 × 224 to 450 × 450. The resulting classification performance of three well established CNNs, namely EfficientNetB0, EfficientNetB1 and SeReNeXt-50 is explored. We also propose and evaluate a multi-scale multi-CNN (MSM-CNN) fusion approach based on a three-level ensemble strategy that utilises the three network architectures trained on cropped dermoscopic images of various scales. Results: Our results show that image cropping is a better strategy compared to image resizing delivering superior classification performance at all explored image scales. Moreover, fusing the results of all three fine-tuned networks using cropped images at all six scales in the proposed MSM-CNN approach boosts the classification performance compared to a single network or a single image scale. On the ISIC 2018 skin lesion classification challenge test set, our MSM-CNN algorithm yields a balanced multi-class accuracy of 86.2% making it the currently second ranked algorithm on the live leaderboard. Conclusions: We confirm that the image size has an effect on skin lesion classification performance when employing transfer learning of CNNs. We also show that image cropping results in better performance compared to image resizing. Finally, a straightforward ensembling approach that fuses the results from images cropped at six scales and three fine-tuned CNNs is shown to lead to the best classification performance.

  • 5.
    Noz, Marilyn E.
    et al.
    New York University.
    Maguire Jr., Gerald Q.
    Columbia University, Department of Computer Science.
    QSH: a minimal but highly portable image display and handling toolkit1988In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 27, no 3, p. 229-240Article in journal (Refereed)
    Abstract [en]

    We describe a software system developed to handle images obtained from different sources, namely, computer-assisted tomography, positron emission tomography, single photon emission tomography and magnetic resonance imaging. In developing the system, it was necessary to address the following points. (1) The types of values that were encountered in both the header information and the pixel elements, namely, integers, floating point numbers, complex numbers and strings. (2) The use of domain-dependent sets of keys, that is, how to choose keys and how to stabilize the use of keys among the user population. This is, for example, how information such as the patient name, or the activity in becquerel is kept. It is necessary to keep both the key values and the units. (3) The development of a method for providing a database using flat files, i.e. linear text. (4) The maintenance of a history of values and operations. This is necessary in order to address the problem of determining from an image was produced. The connection between an image and how it was derived is analogous to describing how a secondary standard is derived from a primary one.

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