Journal of Medical Signals and Sensors
Volume:14 Issue: 1, Jan -Mar 2024

Image enhancement, including image de-noising, super-resolution, registration, reconstruction, in-painting, and so on, is an important issue in different research areas. Different methods which have been exploited for image analysis were mostly based on matrix or low order analysis. However, recent researches show the superior power of tensor-based methods for image enhancement.

In this article, a new method for image super-resolution using Tensor Ring decomposition has been proposed. The proposed image super-resolution technique has been derived for the super-resolution of low resolution and noisy images. The new approach is based on a modification and extension of previous tensor-based approaches used for super-resolution of datasets. In this method, a weighted combination of the original and the resulting image of the previous stage has been computed and used to provide a new input to the algorithm.

This enables the method to do the super-resolution and de-noising simultaneously.

Simulation results show the effectiveness of the proposed approach, especially in highly noisy situations.

The Gleason grading system has been the most effective prediction for prostate cancer patients. This grading system provides this possibility to assess prostate cancer’s aggressiveness and then constitutes an important factor for stratification and therapeutic decisions. However, determining Gleason grade requires highly‑trained pathologists and is time‑consuming and tedious, and suffers from inter‑pathologist variability. To remedy these limitations, this paper introduces an automatic methodology based on transfer learning with pretrained convolutional neural networks (CNNs) for automatic Gleason grading of prostate cancer tissue microarray (TMA).

Fifteen pretrained (CNNs): Efficient Nets (B0‑B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet‑50, SeResnet 50, Xception, DenseNet121, ResNext50, and inception_resnet_v2 were fine‑tuned on a dataset of prostate carcinoma TMA images. Six pathologists separately identified benign and cancerous areas for each prostate TMA image by allocating benign, 3, 4, or 5 Gleason grade for 244 patients. The dataset was labeled by these pathologists and majority vote was applied on pixel‑wise annotations to obtain a unified label.

Results showed the NasnetLarge architecture is the best model among them in the classification of prostate TMA images of 244 patients with accuracy of 0.93 and area under the curve of 0.98.

Our study can act as a highly trained pathologist to categorize the prostate cancer stages with more objective and reproducible results.

In this article, a patient monitoring system is proposed that is able to obtain heart rate and oxygen saturation (SpO2) levels of patients, identify abnormal conditions, and inform emergency status to the nurses. The proposed monitoring system consists of smart patient wristbands, smart nurse wristbands, central monitoring user interface (UI) software, and a wireless communication network. In the proposed monitoring system, a unique smart wristband is dedicated to each of the patients and nurses. To measure heart rate and SpO2 level, a pulse oximeter sensor is used in the patient wristbands. The output of this sensor is transferred to the wristband’s microcontroller where heart rate and SpO2 are calculated through advanced signal processing algorithms. Then, the calculated values are transmitted to central UI software through a wireless network. In the UI software, received values are compared with their normal values and a predefined message is sent to the nurses’ wristband if an abnormal condition is identified. Whenever this message is received by a nurse’s wristband, an acoustic alarm with vibration is generated to inform an emergency status to the nurse. By doing so, health services are delivered to the patients more quickly and as a result, the probability of the patient recovery is increased effectively.

Optical coherence tomography (OCT) imaging has emerged as a promising diagnostic tool, especially in ophthalmology. However, speckle noise and downsampling significantly degrade the quality of OCT images and hinder the development of OCT‑assisted diagnostics. In this article, we address the super‑resolution (SR) problem of retinal OCT images using a statistical modeling point of view.

In the first step, we utilized Weibull mixture model (WMM) as a comprehensive model to establish the specific features of the intensity distribution of retinal OCT data, such as asymmetry and heavy tailed. To fit the WMM to the low‑resolution OCT images, expectation–maximization algorithm is used to estimate the parameters of the model. Then, to reduce the existing noise in the data, a combination of Gaussian transform and spatially constraint Gaussian mixture model is applied. Now, to super‑resolve OCT images, the expected patch log‑likelihood is used which is a patch‑based algorithm with multivariate GMM prior assumption. It restores the high‑resolution (HR) images with maximum a posteriori (MAP) estimator.

The proposed method is compared with some well‑known super‑resolution algorithms visually and numerically. In terms of the mean‑to‑standard deviation ratio (MSR) and the equivalent number of looks, our method makes a great superiority compared to the other competitors.

The proposed method is simple and does not require any special preprocessing or measurements. The results illustrate that our method not only significantly suppresses the noise but also successfully reconstructs the image, leading to improved visual quality.