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This paper proposes a novel method for rapidly and accurately detecting Multiple Sclerosis (MS) lesions and analyzing the progression of lesions and the disease based on differences between histograms of hemispheres and volumetric changes in brain regions over time. The brightness and contrast of pixels are first improved, and MRI slices are then analyzed to detect and eliminate the effects of motion artifacts while imaging. However, an accurate diagnosis tracks changes in volumes of brain regions caused by plaques emerging on brain MRIs in white matter, gray matter, and cerebrospinal fluid (CSF) and the concurrent analysis of differences between histograms of hemispheres. The marker-controlled watershed algorithm was employed to extract MS lesions and plaques. Various MRI centers differ in imaging diameters for which there are no unified standards, leading to different MRI slices. Hence, an individual's two MRI slices of two different occasions are not comparable. Measuring the brain volume can make the proposed method independent of the imaging diameter. This study analyzed the patients with at least three imaging records in the archives of imaging centers. The images were collected from Pars MRI Center and Hajar Hospital MRI Center in Shahrekord, Chararmahal and Bakhtiari Province, Iran. Both centers used Avanto MRI devices and performed imaging at 1 T and 1.5 T, respectively.

The traditional watershed algorithm has the disadvantage of over-segmentation and interference with an image by reflected light. We propose an improved watershed color image segmentation algorithm. It is based on a morphological gradient. This method obtains the component gradient of a color image in a new color space is not disturbed by the reflected light. The gradient image is reconstructed by opening and closing. Therefore, the final gradient image is obtained. The maximum inter-class variance algorithm is used to obtain the threshold automatically for the final gradient image. The original gradient image is forcibly calibrated with the obtained binary labeled image, and the modified gradient image is segmented by watershed. Experimental results show that the proposed method can obtain an accurate and continuous target contour. It will achieve the minimum number of segmentation regions following human vision. Compared with similar algorithms, this way can suppress the meaningless area generated by the reflected light. It will maintain the edge information of the object well. It will improve the robustness and applicability. From the experimental results, it can be seen that compared with the region-growing method and the automatic threshold method; the proposed algorithm has a great improvement in operation efficiency, which increased by 10%. The accuracy and recall rate of the proposed algorithm is more than 0.98. Through the experimental comparison, the advantages of the proposed algorithm in object segmentation can be more intuitively illustrated.

Watershed is a widely used image segmentation algorithm. Most researchers understand just an idea of this method: a grayscale image is considered as topographic relief, which is flooded from initial basins. However, frequently they are not aware of the options of the algorithm and the peculiarities of its realizations. There are many watershed implementations in software packages and products. Even if these packages are based on the identical algorithm–watershed, by flooding their outcomes, processing speed, and consumed memory, vary greatly. In particular, the difference among various implementations is noticeable for huge volumetric images; for instance, tomographic 3D images, for which low performance and high memory requirements of watershed might be bottlenecks. In our review, we discuss the peculiarities of algorithms with and without waterline generation, the impact of connectivity type and relief quantization level on the result, approaches for parallelization, as well as other method options. We present detailed benchmarking of seven open-source and three commercial software implementations of marker-controlled watershed for semantic or instance segmentation. We compare those software packages for one synthetic and two natural volumetric images. The aim of the review is to provide information and advice for practitioners to select the appropriate version of watershed for their problem solving. In addition, we forecast future directions of software development for 3D image segmentation by watershed.

Acute Lymphoblastic Leukemia (ALL), a kind of blood cancer, more frequently observed in the pediatric population, causes rapid production of immature White Blood Cells. Most of the diagnostic techniques like bone marrow aspiration, imaging techniques, etc. are time consuming, error-prone, costly and depend on the skill set of experts. The ultimate goal of this work is to develop a computer aided automatic classification system to classify Benign, Early, Pro-B and Pre-B classes of ALL. Images from the publicly available dataset were subjected to pre-processing and Region of Interest is obtained by adapting the proposed Multilevel Hierarchical Marker-Based Watershed Algorithm (MHMW). A subset of most vital features were selected by utilizing nature inspired metaheuristic Enhanced Glowworm Swarm Optimization (EGSO) algorithm. Popular classifiers -Decision tree, Random Forest, Multi-Layer Perceptron, Naive Bayes and Linear, Polynomial, Radial basis function, sigmoid kernels of Support Vector Machine were used for multiclass classification. Performance of the proposed system has been compared with three other popular optimization algorithms- Particle Swarm Optimization, Artificial Bee Colony Optimization and Elephant Herd Optimization. Random Forest fed with the optimized features obtained from the proposed integration of MHMW and EGSO algorithms outperformed other classifiers with 98.23%, 98.25%, 98.23% of accuracy, precision and F1 score respectively.

Apple leaf diseases significantly impair the photosynthetic efficiency and growth quality of apple trees, leading to reduced fruit yields. Existing methods for disease detection and severity classification struggle to quickly and accurately segment and quantify diseased areas on leaves, particularly in complex backgrounds. To address this issue, we propose a method for assessing the severity of apple leaf diseases based on a combination of improved HRNet and DRL-watershed algorithms. First, we selected HRNet_w32 as the backbone feature extraction network and incorporated a Normalization Attention Mechanism (NAM). Then, we combined the Dice Loss and Focal Loss functions to construct an enhanced HRNet based semantic segmentation model for pixel-level segmentation of both apple leaf and diseased regions. Furthermore, the segmented leaf and disease regions were further optimized using the DRL-watershed algorithm to distinguish overlapping leaf regions. Experimental results demonstrate that the modified HRNet model achieved a mean intersection over union (mIoU) of 88.91% and a mean pixel accuracy (mPA) of 94.13%, representing improvements of 8.77 and 7.25% points, respectively, over the original HRNet. The disease severity assessment accuracy reached 97.65%. This study not only accurately segments apple leaves and diseased areas, but also effectively addresses the impact of complex backgrounds and leaf overlap on disease severity assessment, providing a solid scientific basis for disease management strategies.

Blood cell classification and counting are vital for the diagnosis of various blood-related diseases, such as anemia, leukemia, lymphoma, and thrombocytopenia. The manual process of blood cell classification and counting is time-consuming, prone to errors, and labor-intensive. Therefore, we have proposed a deep learning (DL)-based automated system for blood cell classification and counting from microscopic blood smear images. We classify a total of nine types of blood cells, including Erythrocyte, Erythroblast, Neutrophil, Basophil, Eosinophil, Lymphocyte, Monocyte, Immature Granulocytes, and Platelet. Several preprocessing steps like image resizing, rescaling, contrast enhancement and augmentation are utilized. To segment the blood cells from the entire microscopic images, we employed the U-Net model. This segmentation technique aids in extracting the region of interest (ROI) by removing complex and noisy background elements. Both pixel-level metrics such as accuracy, precision, and sensitivity, and object-level evaluation metrics like Intersection over Union (IOU) and Dice coefficient are considered to comprehensively evaluate the performance of the U-Net model. The segmentation model achieved impressive performance metrics, including 98.23% accuracy, 98.40% precision, 98.26% sensitivity, 95.97% Intersection over Union (IOU), and 97.92% Dice coefficient. Subsequently, a watershed algorithm is applied to the segmented images to separate overlapped blood cells and extract individual cells. We have proposed a BloodCell-Net approach incorporated with custom light weight convolutional neural network (LWCNN) for classifying individual blood cells into nine types. Comprehensive evaluation of the classifier’s performance is conducted using metrics including accuracy, precision, recall, and F1 score. The classifier achieved an average accuracy of 97.10%, precision of 97.19%, recall of 97.01%, and F1 score of 97.10%. A 5-fold cross-validation technique is applied to split the data, which not only aids in reducing overfitting but also helps in generalizing the model.

Ore image segmentation is a key step in an ore grain size analysis based on image processing. The traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and over-segmentation. In this article, in order to solve the problem, an ore image segmentation method based on U-Net is proposed. We adjust the structure of U-Net to speed up the processing, and we modify the loss function to enhance the generalization of the model. After the collection of the ore image, we design the annotation standard and train the network with the annotated image. Finally, the marked watershed algorithm is used to segment the adhesion area. The experimental results show that the proposed method has the characteristics of fast speed, strong robustness and high precision. It has great practical value to the actual ore grain statistical task.

This research introduces an improved method for identifying colorectal tumors through a combination of deep convolutional neural networks (CNNs), transfer learning, and sophisticated image processing techniques used on histopathological images. The suggested ensemble—based on ResNet50 and enhanced with a dual attention mechanism—surpasses individual model architectures by enhancing both accuracy and interpretability, allowing the model to emphasize crucial tissue areas pertinent to diagnosis. Segmentation techniques, such as watershed and distance transform, are utilized to define tumor margins and possible lesion regions. The dataset, obtained from Kather et al. (2019), includes 5,000 histopathological images spanning eight unique categories (tumor, stroma, complex, lymph, debris, mucosa, adipose, empty). The experimental findings demonstrate impressive results, achieving a training accuracy of 98.74%, a validation accuracy of 94.35%, an F1-score of 0.94, a recall of 0.94, a precision of 0.95, a specificity of 0.96, and a Cohen’s kappa score of 0.9354, signifying outstanding inter-class consensus. These results showcase the model’s strength across different class distributions and emphasize its possible clinical value in aiding the early identification and management of colorectal cancer.

Leukemia can be detected by an abnormal rise in the number of immature lymphocytes and by a decrease in the number of other blood cells. To diagnose leukemia, image processing techniques are utilized to examine microscopic peripheral blood smear (PBS) images automatically and swiftly. To the best of our knowledge, the initial step in subsequent processing is a robust segmentation technique for identifying leukocytes from their surroundings. The paper presents the segmentation of leukocytes in which three color spaces are considered in this study for image enhancement. The proposed algorithm uses a marker-based watershed algorithm and peak local maxima. The algorithm was used on three different datasets with various color tones, image resolutions, and magnifications. The average precision for all three-color spaces was the same, i.e. 94% but the Structural Similarity Index Metric (SSIM) and recall of HSV were better than other two. The results of this study will aid experts in narrowing down their options for segmenting leukemia. Based on the comparison, it was concluded that when the colour space correction technique is used, the accuracy of the proposed methodology improves.