Fuzzy-Contextual Contrast Enhancement

Shear wave trajectory detection in ultra-fast M-mode images for liver fibrosis assessment: A deep learning-based line detection approach

Stiffness measurement using shear wave propagation velocity has been the most common non-invasive method for liver fibrosis assessment. The velocity is captured through a trace recorded by transient ultrasonographic elastography, with the slope indicating the velocity of the wave. However, due to various factors such as noise and shear wave attenuation, detecting shear wave trajectory on wave propagation maps is a challenging task. In this work, we made the first attempt to use deep learning methods for shear wave trajectory detection on wave propagation maps. Specifically, we adopted five deep learning models in this task and evaluated them by using a well-acknowledged metric based on EA-Angular-Score (EAA) and task-specific metric based on Young s-Score (Ys) in the line-detection field. Furthermore, we proposed an end-to-end framework based on a Transformer and Hough transform, named Transformer-enhanced Hough Transform (TEHT). It took a wave propagation map as input image and directly output the slope of the shear wave trajectory. The framework extracts multi-scale local features from wave propagation maps, employs a deformable attention mechanism for feature fusion, identifies the target line using the Hough transform's voting mechanism, and calculates the contribution of each scale through channel attention. Wave propagation maps from 68 patients were utilized in this study, with manual annotation performed by a rater who was trained as a radiologist, serving as the reference value. The evaluation revealed that the SLNet model exhibited F-measure of EA and Ys values as 40.33 % and 40.72 %, respectively, while the TEHT model showed F-measure of EA and Ys values as 80.96 % and 98.00 %, respectively. TEHT yielded significantly better performance than other deep learning models. Moreover, TEHT demonstrated strong concordance with the gold standard, yielding R2 values of 0.967 and 0.968 for velocity and liver stiffness, respectively. The present study therefore suggests the application of the TEHT model for assessing liver fibrosis owing to its superiority among the five deep learning models.

A Noise-robust and Overshoot-free Alternative to Unsharp Masking for Enhancing the Acuity of MR Images

Poor acutance of images (unsharpness) is one of the major concerns in magnetic resonance imaging (MRI). MRI-based diagnosis and clinical interventions become difficult due to the vague textural information and weak morphological margins on images. A novel image sharpening algorithm named as maximum local variation-based unsharp masking (MLVUM) to address the issue of 'unsharpness' in MRI is proposed in this paper. In the MLVUM, the sharpened image is the algebraic sum of the input image and the product of the user-defined scale and the difference between the output of a newly designed nonlinear spatial filter named maximum local variation-controlled edge smoothing Gaussian filter (MLVESGF) and the input image, weighted by the normalised MLV. The MLVESGF is a locally adaptive 2D Gaussian edge smoothing kernel whose standard deviation is directly proportional to the local value of the normalized MLV. The values of the acutance-to-noise ratio (ANR) and absolute mean brightness error (AMBE) shown by the MLVUM on 100 MRI slices are 0.6463 ± 0.1852 and 0.3323 ± 0.2200, respectively. Compared to 17 state-of-the-art image sharpening algorithms, the MLVUM exhibited a higher ANR and lower AMBE. The MLVUM selectively enhances the sharpness of edges in the MR images without amplifying the background noise without altering the mean brightness level.

A Context-Based Image Contrast Enhancement Using Energy Equalization With Clipping Limit

In this paper, a new context-based image contrast enhancement process using energy curve equalization (ECE) with a clipping limit has been proposed. In a fundamental anomaly to the existing contrast enhancement practice using histogram equalization, the projected method uses the energy curve. The computation of the energy curve utilizes a modified Hopfield neural network architecture. This process embraces the image's spatial adjacency information to the energy curve. For each intensity level, the energy value is calculated and the overall energy curve appears to be smoother than the histogram. A clipping limit applies to evade the over enhancement and is chosen as the average of the mean and median value. The clipped energy curve is subdivided into three regions based on the standard deviation value. Each part of the subdivided energy curve is equalized individually, and the final enhanced image is produced by combining transfer functions computed by the equalization process. The projected scheme's qualitative and quantitative efficiency is assessed by comparing it with the conventional histogram equalization techniques with and without the clipping limit.

A novel method based on convolutional neural network for malaria diagnosis

Malaria is one of the three major diseases with the highest mortality worldwide and can turn fatal if not taken seriously. The key to surviving this disease is its early diagnosis. However, manual diagnosis is time consuming and tedious due to the large amount of image data. Generally, computer-aided diagnosis can effectively improve doctors’ perception and accuracy. This paper presents a medical diagnosis method powered by convolutional neural network (CNN) to extract features from images and improve early detection of malaria. The image sharpening and histogram equalization method are used aiming at enlarging the difference between parasitized regions and other area. Dropout technology is employed in every convolutional layer to reduce overfitting in the network, which is proved to be effective. The proposed CNN model achieves a significant performance with the best classification accuracy of 99.98%. Moreover, this paper compares the proposed model with the pretrained CNNs and other traditional algorithms. The results indicate the proposed model can achieve state-of-the-art performance from multiple metrics. In general, the novelty of this work is the reduction of the CNN structure to only five layers, thereby greatly reducing the running time and the number of parameters, which is demonstrated in the experiments. Furthermore, the proposed model can assist clinicians to accurately diagnose the malaria disease.

Low-contrast X-ray enhancement using a fuzzy gamma reasoning model

X-ray images play an important role in providing physicians with satisfactory information correlated to fractures and diseases; unfortunately, most of these images suffer from low contrast and poor quality. Thus, enhancement of the image will increase the accuracy of correct information on pathologies for an autonomous diagnosis system. In this paper, a new approach for low-contrast X-ray image enhancement based on brightness adjustment using a fuzzy gamma reasoning model (FGRM) is proposed. To achieve this, three phases are considered: pre-processing, Fuzzy model for adaptive gamma correction (GC), and quality assessment based on blind reference. The proposed approach's accuracy is examined through two different blind reference approaches based on statistical measures (BR-SM) and dispersion-location (BR-DL) descriptors, supported by resulting images. Experimental results of the proposed FGRM approach on three databases (cervical, lumbar, and hand radiographs) yield favorable results in terms of contrast adjustment and providing satisfactory quality images. Graphical Abstract Graphical abstract of the proposed enhancement method.