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  1. Article ; Online: A Two-Stage Automatic Color Thresholding Technique.

    Pootheri, Shamna / Ellam, Daniel / Grübl, Thomas / Liu, Yang

    Sensors (Basel, Switzerland)

    2023  Volume 23, Issue 6

    Abstract: Thresholding is a prerequisite for many computer vision algorithms. By suppressing the background in an image, one can remove unnecessary information and shift one's focus to the object of inspection. We propose a two-stage histogram-based background ... ...

    Abstract Thresholding is a prerequisite for many computer vision algorithms. By suppressing the background in an image, one can remove unnecessary information and shift one's focus to the object of inspection. We propose a two-stage histogram-based background suppression technique based on the chromaticity of the image pixels. The method is unsupervised, fully automated, and does not need any training or ground-truth data. The performance of the proposed method was evaluated using a printed circuit assembly (PCA) board dataset and the University of Waterloo skin cancer dataset. Accurately performing background suppression in PCA boards facilitates the inspection of digital images with small objects of interest, such as text or microcontrollers on a PCA board. The segmentation of skin cancer lesions will help doctors to automate skin cancer detection. The results showed a clear and robust background-foreground separation across various sample images under different camera or lighting conditions, which the naked implementation of existing state-of-the-art thresholding methods could not achieve.
    Language English
    Publishing date 2023-03-22
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23063361
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Localisation of Mammographic masses by Greedy Backtracking of Activations in the Stacked Auto-Encoders

    Pootheri, Shamna / K, Govindan V

    2023  

    Abstract: Mammographic image analysis requires accurate localisation of salient mammographic masses. In mammographic computer-aided diagnosis, mass or Region of Interest (ROI) is often marked by physicians and features are extracted from the marked ROI. In this ... ...

    Abstract Mammographic image analysis requires accurate localisation of salient mammographic masses. In mammographic computer-aided diagnosis, mass or Region of Interest (ROI) is often marked by physicians and features are extracted from the marked ROI. In this paper, we present a novel mammographic mass localisation framework, based on the maximal class activations of the stacked auto-encoders. We hypothesize that the image regions activating abnormal classes in mammographic images will be the breast masses which causes the anomaly. The experiment is conducted using randomly selected 200 mammographic images (100 normal and 100 abnormal) from IRMA mammographic dataset. Abnormal mass regions marked by an expert radiologist are used as the ground truth. The proposed method outperforms existing Deep Convolutional Neural Network (DCNN) based techniques in terms of salient region detection accuracy. The proposed greedy backtracking method is more efficient and does not require a vast number of labelled training images as in DCNN based method. Such automatic localisation method will assist physicians to make accurate decisions on biopsy recommendations and treatment evaluations.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-05-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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