ELCVIA Electronic Letters on Computer Vision and Image Analysis

Color Image Visual Secret Sharing with Expressive Shares using Color to Gray & Back and Cosine Transform

Ratnesh Naresh Chaturvedi — 2023-03-29

Color Visual Secret Sharing (VSS) is an essential form of VSS. It is so because nowadays, most people like to share visual data as a color image. There are color VSS schemes capable of dealing with halftone color images or color images with selected colors, and some dealing with natural color images, which generate low quality of recovered secret. The proposed scheme deals with a color image in the RGB domain and generates gray shares for color images using color to gray and back through compression. These shares are encrypted into an innocent-looking gray cover image using a Discrete Cosine Transform (DCT) to make meaningful shares. Reconstruct a high-quality color image through the gray shares extracted from an innocent-looking gray cover image. Thus, using lower bandwidth for transmission and less storage.

Improved Classification of Histopathological images using the feature fusion of Thepade sorted block truncation code and Niblack thresholding

Sudeep D Thepade — 2023-06-25

Histopathology is the study of disease-affected tissues, and it is particularly helpful in diagnosis and figuring out how severe and rapidly a disease is spreading. It also demonstrates how to recognize a variety of human tissues and analyze the alterations brought on by sickness. Only through histopathological pictures can a specific collection of disease characteristics, such as lymphocytic infiltration of malignancy, be determined. The "gold standard" for diagnosing practically all cancer forms is a histopathological picture. Diagnosis and prognosis of cancer at an early stage are essential for treatment, which has become a requirement in cancer research. The importance and advantages of classification of cancer patients into more-risk or less-risk divisions have motivated many researchers to study and improve the application of machine learning (ML) methods. It would be interesting to explore the performance of multiple ML algorithms in classifying these histopathological images. Something crucial in this field of ML for differentiating images is feature extraction. Features are the distinctive identifiers of an image that provide a brief about it. Features are drawn out for discrimination between the images using a variety of handcrafted algorithms. This paper presents a fusion of features extracted with Thepade sorted block truncation code (TSBTC) and Niblack thresholding algorithm for the classification of histopathological images. The experimental validation is done using 960 images present in the Kimiapath-960 dataset of histopathological images with the help of performance metrics like sensitivity, specificity and accuracy. Better performance is observed by an ensemble of TSBTC N-ary and Niblack's thresholding features as 97.92% of accuracy in 10-fold cross-validation.

Infrared Thermography For Seal Defects Detection On Packaged Products: Unbalanced Machine Learning Classification With Iterative Digital Image Restoration

Victor Guillot — 2023-06-25

Non-destructive and online defect detection on seals is increasingly being deployed in packaging processes, especially for food and pharmaceutical products. It is a key control step in these processes as it curtails the costs of these defects.

To address this cause, this paper highlights a combination of two cost-effective methods, namely machine learning algorithms and infrared thermography. Expectations can, however, be restricted when the training data is small, unbalanced, and subject to optical imperfections.

This paper proposes a classification method that tackles these limitations. Its accuracy exceeds 93% with two small training sets, including 2.5 to 10 times fewer negatives. Its algorithm has a low computational cost compared to deep learning approaches, and does not need any prior statistical studies on defects characterization.

Deep Learning Based Localisation and Segmentation of Prostate Cancer from mp-MRI Images

takwa Ben Aïcha — 2023-06-27

Prostate Cancer (PCa) is one of the most common diseases in adult males. Currently, mp-MRI imaging represents the most promising technique for screening, diagnosing, and managing this cancer. However, the multiple mp-MRI sequences' visual interpretation is not straightforward and may present crucial inter-reader variability in the diagnosis, especially when the images contradict each other. In this work, we propose a computer-aided diagnostic system to assist the radiologist in
locating and segmenting prostate lesions. As fully convolutional neural networks (UNet) have proved themselves the leading algorithm for biomedical image segmentation, we investigate their use to find PCa lesions and segment for accurate lesions contours jointly. We offer a fully automatic system via MultiResUNet, initially proposed to segment skin cancer. We trained and validated an altered version of the MultiResUnet model using an augmented Radboudumc prostate cancer dataset and obtained encouraging results. An accuracy of 98.34\% is achieved, outperforming the concurrent system based on deep architecture.