An approach for cross-modality guided quality enhancement of liver image

A novel approach for multimodal liver image contrast enhancement is put forward in this paper. The proposed approach utilizes magnetic resonance imaging (MRI) scan of liver as a guide to enhance the structures of computed tomography (CT) liver. The enhancement process consists of two phases: The first phase is the transformation of MRI and CT modalities to be in the same range. Then the histogram of CT liver is adjusted to match the histogram of MRI. In the second phase, an adaptive histogram equalization technique is presented by splitting the CT histogram into two sub-histograms and replacing their cumulative distribution functions with two smooths sigmoid. The subjective and objective assessments of experimental results indicated that the proposed approach yields better results. In addition, the image contrast is effectively enhanced as well as the mean brightness and details are well preserved

Digging for gold: evaluating the authenticity of saffron (Crocus sativus L.) via deep learning optimization

IntroductionSaffron is one of the most coveted and one of the most tainted products in the global food market. A major challenge for the saffron industry is the difficulty to distinguish between adulterated and authentic dried saffron along the supply chain. Current approaches to analyzing the intrinsic chemical compounds (crocin, picrocrocin, and safranal) are complex, costly, and time-consuming. Computer vision improvements enabled by deep learning have emerged as a potential alternative that can serve as a practical tool to distinguish the pureness of saffron.MethodsIn this study, a deep learning approach for classifying the authenticity of saffron is proposed. The focus was on detecting major distinctions that help sort out fake samples from real ones using a manually collected dataset that contains an image of the two classes (saffron and non-saffron). A deep convolutional neural model MobileNetV2 and Adaptive Momentum Estimation (Adam) optimizer were trained for this purpose.ResultsThe observed metrics of the deep learning model were: 99% accuracy, 99% recall, 97% precision, and 98% F-score, which demonstrated a very high efficiency.DiscussionA discussion is provided regarding key factors identified for obtaining positive results. This novel approach is an efficient alternative to distinguish authentic from adulterated saffron products, which may be of benefit to the saffron industry from producers to consumers and could serve to develop models for other spices

Multi-Phase Information Theory-Based Algorithm for Edge Detection of Aerial Images

Edge detection is the diverse way used to detect boundaries in digital images. Many methods exist to achieve this purpose, yet not all of them can produce results with high detection ratios. Some may have high complexity, and others may require numerous inputs. Therefore, a new multi-phase algorithm that depends on information theory is introduced in this article to detect the edges of aerial images adequately in a fully automatic manner. The proposed algorithm operated by utilizing Shannon and Hill entropies with specific rules along with a non-complex edge detector to record the vital edge information. The proposed algorithm was examined with different aerial images, its performances appraised against six existing approaches, and the outcomes were assessed using three image evaluation methods. From the results, promising performances were recorded as the proposed algorithm performed the best in many aspects and provided satisfactory results. The results of the proposed algorithm had high edge detection ratios as it was able to capture most of the significant edges of the given images. Such findings make the proposed algorithm desirable to be used as a key image detection method with other image-related applications

Segmentation of Spectral Plant Images Using Generative Adversary Network Techniques

The spectral image analysis of complex analytic systems is usually performed in analytical chemistry. Signals associated with the key analytics present in an image scene are extracted during spectral image analysis. Accordingly, the first step in spectral image analysis is to segment the image in order to extract the applicable signals for analysis. In contrast, using traditional methods of image segmentation in chronometry makes it difficult to extract the relevant signals. None of the approaches incorporate contextual information present in an image scene; therefore, the classification is limited to thresholds or pixels only. An image translation pixel-to-pixel (p2p) method for segmenting spectral images using a generative adversary network (GAN) is presented in this paper. The p2p GAN forms two neuronal models. During the production and detection processes, the representation learns how to segment ethereal images precisely. For the evaluation of the results, a partial discriminate analysis of the least-squares method was used to classify the images based on thresholds and pixels. From the experimental results, it was determined that the GAN-based p2p segmentation performs the best segmentation with an overall accuracy of 0.98 ± 0.06. This result shows that image processing techniques using deep learning contribute to enhanced spectral image processing. The outcomes of this research demonstrated the effectiveness of image-processing techniques that use deep learning to enhance spectral-image processing

A Novel Algorithm for Edge Detection of Noisy Medical Images

Medical image edge detection is an important work for object recognition of the human organs, and it is an essential pre-processing step in medical image segmentation and 3D reconstruction. Although many edge-detection evaluation methods have been developed in the past years, however this is still a challenging and unsolved problem. Conventionally, edge is detected according to some early brought forward algorithms like Canny, LOG, Sobel, Prewitt, Roberts algorithms but in theory they belong to the high pass filtering, which are not fit for noise medical image edge detection because noise and edge belong to the scope of high frequency. In real world applications, medical images contain object boundaries and object shadows and noise. Therefore, they may be difficult to distinguish the exact edge from noise or trivial geometric features. After studying all traditional methods of edge detection, it has been analyzed that for these situations, a new algorithm is needed which is optimal. In this paper, we propose a new algorithm for edge detection of noisy medical images based on both Tsallis and Shannon entropy together. The performance of our method is compared against other methods by using blood cells image corrupted with various levels of "salt and pepper". It is observed that the proposed algorithm displayed superior noise resilience and decrease the computation time