Tamil handwritten palm leaf manuscript dataset (THPLMD)

Most palm leaf manuscripts are generally accessible in deteriorated condition, including cracks, discoloration, moisture and humidity, and insects bite. Such a manuscript is considered challenging in the research field. We captured deteriorated Tamil palm leaves around 262 dataset samples are 'Naladiyar(27)',' Tholkappiyam(221)', and' Thirikadugam(14)' which are genned up mortal health, discipline, authoritative text on Tamil grammar. We contribute the high-quality raw dataset with the aid of a Nikon camera, pre-enhance samples by editing software tool, and applied the Otsu threshold to deliver the ground images through binarization as readily accessible content presenting a highly time-consuming task to play a vital role in Machine/Deep/ Transfer learning, AI, and ANN.

Improving the accuracy of cotton seedling emergence rate estimation by fusing UAV-based multispectral vegetation indices

Timely and accurate estimation of cotton seedling emergence rate is of great significance to cotton production. This study explored the feasibility of drone-based remote sensing in monitoring cotton seedling emergence. The visible and multispectral images of cotton seedlings with 2 - 4 leaves in 30 plots were synchronously obtained by drones. The acquired images included cotton seedlings, bare soil, mulching films, and PE drip tapes. After constructing 17 visible VIs and 14 multispectral VIs, three strategies were used to separate cotton seedlings from the images: (1) Otsu's thresholding was performed on each vegetation index (VI); (2) Key VIs were extracted based on results of (1), and the Otsu-intersection method and three machine learning methods were used to classify cotton seedlings, bare soil, mulching films, and PE drip tapes in the images; (3) Machine learning models were constructed using all VIs and validated. Finally, the models constructed based on two modeling strategies [Otsu-intersection (OI) and machine learning (Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)] showed a higher accuracy. Therefore, these models were selected to estimate cotton seedling emergence rate, and the estimates were compared with the manually measured emergence rate. The results showed that multispectral VIs, especially NDVI, RVI, SAVI, EVI2, OSAVI, and MCARI, had higher crop seedling extraction accuracy than visible VIs. After fusing all VIs or key VIs extracted based on Otsu's thresholding, the binary image purity was greatly improved. Among the fusion methods, the Key VIs-OI and All VIs-KNN methods yielded less noises and small errors, with a RMSE (root mean squared error) as low as 2.69% and a MAE (mean absolute error) as low as 2.15%. Therefore, fusing multiple VIs can increase crop image segmentation accuracy. This study provides a new method for rapidly monitoring crop seedling emergence rate in the field, which is of great significance for the development of modern agriculture.

Comparison of Otsu and an adapted Chan-Vese method to determine thyroid active volume using Monte Carlo generated SPECT images

BACKGROUND

The Otsu method and the Chan-Vese model are two methods proven to perform well in determining volumes of different organs and specific tissue fractions. This study aimed to compare the performance of the two methods regarding segmentation of active thyroid gland volumes, reflecting different clinical settings by varying the parameters: gland size, gland activity concentration, background activity concentration and gland activity concentration heterogeneity.

METHODS

A computed tomography was performed on three playdough thyroid phantoms with volumes 20, 35 and 50 ml. The image data were separated into playdough and water based on Hounsfield values. Sixty single photon emission computed tomography (SPECT) projections were simulated by Monte Carlo method with isotope Technetium-99 m ([Formula: see text]Tc). Linear combinations of SPECT images were made, generating 12 different combinations of volume and background: each with both homogeneous thyroid activity concentration and three hotspots of different relative activity concentrations (48 SPECT images in total). The relative background levels chosen were 5 %, 10 %, 15 % and 20 % of the phantom activity concentration and the hotspot activities were 100 % (homogeneous case) 150 %, 200 % and 250 %. Poisson noise, (coefficient of variation of 0.8 at a 20 % background level, scattering excluded), was added before reconstruction was done with the Monte Carlo-based SPECT reconstruction algorithm Sahlgrenska Academy reconstruction code (SARec). Two different segmentation algorithms were applied: Otsu's threshold selection method and an adaptation of the Chan-Vese model for active contours without edges; the results were evaluated concerning relative volume, mean absolute error and standard deviation per thyroid volume, as well as dice similarity coefficient.

RESULTS

Both methods segment the images well and deviate similarly from the true volumes. They seem to slightly overestimate small volumes and underestimate large ones. Different background levels affect the two methods similarly as well. However, the Chan-Vese model deviates less and paired t-testing showed significant difference between distributions of dice similarity coefficients (p-value [Formula: see text]).

CONCLUSIONS

The investigations indicate that the Chan-Vese model performs better and is slightly more robust, while being more challenging to implement and use clinically. There is a trade-off between performance and user-friendliness.

Multilevel Threshold Segmentation of Skin Lesions in Color Images Using Coronavirus Optimization Algorithm

Skin Cancer (SC) is among the most hazardous due to its high mortality rate. Therefore, early detection of this disease would be very helpful in the treatment process. Multilevel Thresholding (MLT) is widely used for extracting regions of interest from medical images. Therefore, this paper utilizes the recent Coronavirus Disease Optimization Algorithm (COVIDOA) to address the MLT issue of SC images utilizing the hybridization of Otsu, Kapur, and Tsallis as fitness functions. Various SC images are utilized to validate the performance of the proposed algorithm. The proposed algorithm is compared to the following five meta-heuristic algorithms: Arithmetic Optimization Algorithm (AOA), Sine Cosine Algorithm (SCA), Reptile Search Algorithm (RSA), Flower Pollination Algorithm (FPA), Seagull Optimization Algorithm (SOA), and Artificial Gorilla Troops Optimizer (GTO) to prove its superiority. The performance of all algorithms is evaluated using a variety of measures, such as Mean Square Error (MSE), Peak Signal-To-Noise Ratio (PSNR), Feature Similarity Index Metric (FSIM), and Normalized Correlation Coefficient (NCC). The results of the experiments prove that the proposed algorithm surpasses several competing algorithms in terms of MSE, PSNR, FSIM, and NCC segmentation metrics and successfully solves the segmentation issue.

Otsu Multi-Threshold Image Segmentation Based on Adaptive Double-Mutation Differential Evolution

A quick and effective way of segmenting images is the Otsu threshold method. However, the complexity of time grows exponentially as the number of thresolds rises. The aim of this study is to address the issues with the standard threshold image segmentation method's low segmentation effect and high time complexity. The two mutations differential evolution based on adaptive control parameters is presented, and the twofold mutation approach and adaptive control parameter search mechanism are used. Superior double-mutation differential evolution views Otsu threshold picture segmentation as an optimization issue, uses the maximum interclass variance technique as the objective function, determines the ideal threshold, and then implements multi-threshold image segmentation. The experimental findings demonstrate the robustness of the enhanced double-mutation differential evolution with adaptive control parameters. Compared to other benchmark algorithms, our algorithm excels in both image segmentation accuracy and time complexity, offering superior performance.

Improving the segmentation of digital images by using a modified Otsu's between-class variance

Image segmentation is a critical stage in the analysis and pre-processing of images. It comprises dividing the pixels according to threshold values into several segments depending on their intensity levels. Selecting the best threshold values is the most challenging task in segmentation. Because of their simplicity, resilience, reduced convergence time, and accuracy, standard multi-level thresholding (MT) approaches are more effective than bi-level thresholding methods. With increasing thresholds, computer complexity grows exponentially. A considerable number of metaheuristics were used to optimize these problems. One of the best image segmentation methods is Otsu's between-class variance. It maximizes the between-class variance to determine image threshold values. In this manuscript, a new modified Otsu function is proposed that hybridizes the concept of Otsu's between class variance and Kapur's entropy. For Kapur's entropy, a threshold value of an image is selected by maximizing the entropy of the object and background pixels. The proposed modified Otsu technique combines the ability to find an optimal threshold that maximizes the overall entropy from Kapur's and the maximum variance value of the different classes from Otsu. The novelty of the proposal is the merging of two methodologies. Clearly, Otsu's variance could be improved since the entropy (Kapur) is a method used to verify the uncertainty of a set of information. This paper applies the proposed technique over a set of images with diverse histograms, which are taken from Berkeley Segmentation Data Set 500 (BSDS500). For the search capability of the segmentation methodology, the Arithmetic Optimization algorithm (AOA), the Hybrid Dragonfly algorithm, and Firefly Algorithm (HDAFA) are employed. The proposed approach is compared with the existing state-of-art objective function of Otsu and Kapur. Qualitative experimental outcomes demonstrate that modified Otsu is highly efficient in terms of performance metrics such as PSNR, mean, threshold values, number of iterations taken to converge, and image segmentation quality.

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu

Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; it is also a good feed for the development of animal husbandry and essential raw material for light industry, chemical industry, medicine, and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification. However, one single method cannot achieve a good segmentation effect to meet the diversity and complexity of disease spots. In order to solve the shortcomings of noise interference and oversegmentation in the Otsu segmentation method, a non-local mean filtered two-dimensional histogram was used to remove the noise in disease images and a new elite strategy improved comprehensive particle swarm optimization (PSO) method was used to find the optimal segmentation threshold of the objective function in this study. The experimental results of segmenting three kinds of maize foliar disease images show that the segmentation effect of this method is better than other similar algorithms and it has better convergence and stability.

Automatic cell counting for phase-contrast microscopic images based on a combination of Otsu and watershed segmentation method

Cell counting plays a vital role in biomedical researches. However, manual cell counting is time-consuming, laborious, and low efficiency and has a high counting error rate problem. An automatic counting approach for Hela cells of phase-contrast microscopic images is proposed based on the combination of Otsu and watershed segmentation methods to solve the mentioned issues. Firstly, image preprocessing is performed. Secondly, the Otsu method was used to obtain an automatic global optimal threshold for segmentation to achieve batch counting of images. Thirdly, the marker watershed was performed to separate adherent cells and to avoid over-segmentation simultaneously. Finally, cells in phase-contrast microscopic images were counted by detecting the numbers of connected domains in the binary image. Taking the manual counting result as the counting standard and MIS, INC, and ACC are used as evaluation indicators. The experimental results showed that the average values of MIS, INC, and ACC of the proposed method are only 3.31%, 3.49%, and 96.69%, respectively. Additionally, each cell image was counted only takes 0.65 s on averagely. To further test the performance of the proposed method, a comparative experiment was carried out by Image J, and the result shows that the proposed method has a better counting performance with a higher average accuracy of 96.55% to Image J with 93.39%.The proposed method for cell counting is simple, feasible, fast and high accurate, and it can be used as an effective method for cell counting of the phase-contrast microscopic images.

Automatic flood detection using sentinel-1 images on the google earth engine

Flood is considered to be one of the most destructive natural disasters. It is important to detect the flood-affected area in a reasonable time. In March 2019, a severe flood occurred in the north of Iran and lasted for 2 months. In the present paper, this flood event has been monitored by Sentinel-1 images. The Otsu thresholding algorithm has been applied to separate flooded areas from remaining land covers. The threshold value of -14.9 dB was derived and applied to each scene to delineate flooded areas. There was high variability of the inundated area; however, the presented threshold correctly represented the variation of the flood. The resultant maps were further verified by independent datasets. The overall accuracies were higher than 90%, confirming the applicability of the Otsu automatic thresholding method in flood mapping. The automatic approach is efficient in rapid fold mapping across complex landscapes.

An image-based approach for quantitative assessment of uniformity in particle distribution of noise reduction material

Under the background of noise pollution caused by railway development, noise reduction material is worthy of in-depth study. A uniform distribution of particles in the material has an important influence on sound absorption property. In this article, the relevant image processing technology is applied to get structure information to quantify the uniformity. The main contributions of this study are: (a) In the preprocessing stage, SEM cross-sectional image of material is processed by mean filter and histogram equalization. Therefore, the grayscale and the contrast between target and background are enhanced, and a low-quality image is transformed into a high-quality one. (b) In the locating stage, local details of the image are considered to discriminate each particle from the whole image. When a global threshold is combined with the local iteration threshold, an improved Otsu algorithm is designed to binarize the image. Through morphology transforming, area filtering, and hole filling, the connected domain of target can be found and particles are located. (c) In the assessing stage, area index, number index and local distance index are established for assessing the uniformity of pore distribution. The experimental results indicate that statistical analysis is consistent with human visual observation. The smaller the porosity is, the better the uniformity is. Compared with some important methods, the effectiveness and efficiency of the proposed approach could be illustrated.

Segmentation of Microscope Erythrocyte Images by CNN-Enhanced Algorithms

This paper presents an algorithm for segmentation and shape analysis of erythrocyte images collected using an optical microscope. The main objective of the proposed approach is to compute statistical object values such as the number of erythrocytes in the image, their size, and width to height ratio. A median filter, a mean filter and a bilateral filter were used for initial noise reduction. Background subtraction using a rolling ball filter removes background irregularities. Combining the distance transform with the Otsu and watershed segmentation methods allows for initial image segmentation. Further processing steps, including morphological transforms and the previously mentioned segmentation methods, were applied to each segmented cell, resulting in an accurate segmentation. Finally, the noise standard deviation, sensitivity, specificity, precision, negative predictive value, accuracy and the number of detected objects are calculated. The presented approach shows that the second stage of the two-stage segmentation algorithm applied to individual cells segmented in the first stage allows increasing the precision from 0.857 to 0.968 for the artificial image example tested in this paper. The next step of the algorithm is to categorize segmented erythrocytes to identify poorly segmented and abnormal ones, thus automating this process, previously often done manually by specialists. The presented segmentation technique is also applicable as a probability map processor in the deep learning pipeline. The presented two-stage processing introduces a promising fusion model presented by the authors for the first time.

Evaluation of Microwave and Ozone Disinfections on the Color Characteristics of Iranian Export Raisins through an Image Processing Technique

Raisins are one of the most important Iranian export products but are threatened by various storage pests. Because of the disadvantages of fumigants, we evaluated alternative microwave and ozone methods for their disinfection and the side effects on raisin qualities. To perform microwave disinfection, the studied raisin samples were exposed to microwave powers of 450, 720, and 900 W for 20, 30, 40, and 50 s. Also, ozone treatments included various combinations of ozone concentrations (2, 3, and 5 ppm) and exposure times (15, 30, 45, 60, and 90 min). An image processing technique was implemented to determine the color changes of raisins in terms of lightness, redness, yellowness, total color difference, chroma, and hue angle. The results revealed that increasing the microwave power and exposure time might lead to further changes of the previously mentioned color characteristics. Compared with the microwave treatments, ozone treatments had fewer effects on those features. Generally, microwave and ozone methods could successfully disinfect Oryzaephilus surinamensis in raisins, with acceptable changes in all the color characteristics. Hence, the previously mentioned methods are proposed as alternative chemical fumigants.