Advancing multimodal medical image fusion: an adaptive image decomposition approach based on multilevel Guided filtering.

With the rapid development of medical imaging methods, multimodal medical image fusion techniques have caught the interest of researchers. The aim is to preserve information from diverse sensors using various models to generate a single informative image. The main challenge is to derive a trade-off between the spatial and spectral qualities of the resulting fused image and the computing efficiency. This article proposes a fast and reliable method for medical image fusion depending on multilevel Guided edge-preserving filtering (MLGEPF) decomposition rule. First, each multimodal medical image was divided into three sublayer categories using an MLGEPF decomposition scheme: small-scale component, large-scale component and background component. Secondly, two fusion strategies-pulse-coupled neural network based on the structure tensor and maximum based-are applied to combine the three types of layers, based on the layers' various properties. The three different types of fused sublayers are combined to create the fused image at the end. A total of 40 pairs of brain images from four separate categories of medical conditions were tested in experiments. The pair of images includes various case studies including magnetic resonance imaging (MRI) , TITc, single-photon emission computed tomography (SPECT) and positron emission tomography (PET). We included qualitative analysis to demonstrate that the visual contrast between the structure and the surrounding tissue is increased in our proposed method. To further enhance the visual comparison, we asked a group of observers to compare our method's outputs with other methods and score them. Overall, our proposed fusion scheme increased the visual contrast and received positive subjective review. Moreover, objective assessment indicators for each category of medical conditions are also included. Our method achieves a high evaluation outcome on feature mutual information (FMI), the sum of correlation of differences (SCD), Qabf and Qy indexes. This implies that our fusion algorithm has better performance in information preservation and efficient structural and visual transferring.

Endoscopic Image Enhancement: Wavelet Transform and Guided Filter Decomposition-Based Fusion Approach.

Endoscopies are helpful for examining internal organs, including the gastrointestinal tract. The endoscope device consists of a flexible tube to which a camera and light source are attached. The diagnostic process heavily depends on the quality of the endoscopic images. That is why the visual quality of endoscopic images has a significant effect on patient care, medical decision-making, and the efficiency of endoscopic treatments. In this study, we propose an endoscopic image enhancement technique based on image fusion. Our method aims to improve the visual quality of endoscopic images by first generating multiple sub images from the single input image which are complementary to one another in terms of local and global contrast. Then, each sub layer is subjected to a novel wavelet transform and guided filter-based decomposition technique. To generate the final improved image, appropriate fusion rules are utilized at the end. A set of upper gastrointestinal tract endoscopic images were put to the test in studies to confirm the efficacy of our strategy. Both qualitative and quantitative analyses show that the proposed framework performs better than some of the state-of-the-art algorithms.

Smart and low-cost fluorometer for identifying breast cancer malignancy based on lipid droplets accumulation.

The most common cause of breast cancer-related death is tumor recurrence. To develop more effective treatments, the identification of cancer cell specific malignancy indicators is therefore critical. Lipid droplets are known as an emerging hallmark in aggressive breast tumors. A common technique that can be used for observing molecules in cancer microenvironment is fluorescence microscopy. We describe the design, development and applicability of a smart fluorometer to detect lipid droplet accumulation based on the emitted fluorescence signals from highly malignant (MDA-MB-231) and mildly malignant (MCF7) breast cancer cell lines, that are stained with BODIPY dye. This device uses a visible-range light source as an excitation source and a spectral sensor as the detector. A commercial imaging system was used to examine the fluorescent cancer cell lines before being validated in a preclinical setting with the developed prototype. The outcomes indicate that this low-cost fluorometer can effectively detect the alterations levels of lipid droplets and hence distinguish between "moderately malignant" and "highly malignant" cancer cells. In comparison to prior research that used fluorescence spectroscopy techniques to detect cancer biomarkers, this study revealed enhanced capability in classifying mildly and highly malignant cancer cell lines.