A novel method for recognizing fruits with plastic packing

Pineapple is one of the potential commodities in Indonesia. This is due to high market demand, potential suitable land in Indonesia and public awareness of fruit supply. The main factor of crop failure in pineapple plants is the delay in handling pineapple plant diseases. To identify in image processing requires class grouping. Self-Organizing Map (SOM) which divides the input pattern into several groups so that the network output is in the form of the group that is most similar to the given input. However, the SOM algorithm requires data input that characterizes an object to facilitate the identification process. So, in this study the SOM algorithm was improved through color feature extraction with parameters Red, Green, Blue, Hue, Saturation and Value, as well as texture feature extraction with parameters contrast, correlation, energy, and homogeneity in the Gray Level Co-occurrence Matrix (GLCM). Based on the results of tests carried out by the SOM algorithm with color and texture feature extraction parameters, it is able to assist in increasing the accuracy value. The results of testing the SOM model with color and texture feature extraction obtained a precision value of 93.33%, recall of 92.31% and accuracy of 92.78%.

Nowadays, humans tend to carry out activities during the day, both indoors and outdoors. Activities carried out outdoors cause human skin to often receive direct exposure to sunlight, which contains ultraviolet (UV) rays. Direct exposure to UV rays on the skin will harm the skin's health, which is the covering of the human body. Harmful effects on the skin usually include the skin becoming dark and dull, burns, and even causes cancer. One of the skin cancers that may appear on human skin is Actinic Keratosis (AK) cancer. AK cancer is a type of cancer that is classified as benign and can be cured with medical help. However, if this cancer is not caught early, it can become Squamous Cell Carcinoma (SCC), a type of malignant cancer. This research aims to design a system for identifying AK cancer types using color and texture feature extraction. RGB color feature extraction is obtained from image color segmentation and RGB values. The Gray Level Co-occurrence Matrix (GLCM) method is used to determine the texture of the skin cancer. Identification is carried out by a classification process using a Support Vector Machine (SVM), which can recognize the type of AK cancer. This research uses three classification methods: classification with color extraction, classification with texture extraction, and classification with color and texture extraction. Research shows that the highest level of accuracy in cancer recognition reaches 96% by combining color and texture extraction results as classification determinants. So, the system designed has succeeded in recognizing the type of AK cancer early on..

Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification. Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.

Snapper is one of the favorite fish for consumption because it has a myriad of benefits for the human body. There are many types of snapper, especially snapper which is often found in Indonesian waters. Knowing the types of snapper is important knowledge because snapper has different characteristics, for example there are snapper that can be consumed and there are also types of snapper that can be cultivated. However, the lack of information and similar types of snapper makes it difficult for people to identify the type of snapper. This study aims to implement a Self-Organizing Map (SOM) artificial neural network for classification of snapper species based on color and texture characteristics. In order to provide information about the snapper object to be classified, color and texture feature extraction is used. In color feature extraction, RGB and HSV parameters are used and for texture features, the Gray Level Co-occurrence Matrix (GLCM) approach is applied. Furthermore, the characteristic results obtained will be classified using the Self-Organizing Map (SOM) algorithm which divides the input patterns into certain classes so that the network output is in the form of classes that have similarities to the given input. Based on the results of the accuracy test, the built model is capable of producing an accuracy of 89.89%. Thus, the SOM model built for image classification of snapper species is in the good category.

Many people are being affected by eczema around the world. In the Philippines, the most common types of eczema are atopic dermatitis, contact dermatitis, and nummular dermatitis. This study covered these three types and detected them by applying image processing techniques, Gray Level Co-occurrence Matrix, and the classifier Support Vector Machine. The designed prototype is to capture a section of the skin where eczema can be present and send the image to the software for skin region detection, eczema region detection, and feature extractions. In skin region detection, the YCbCr color model identifies the skin's color to discard the non-skin pixels and detect the skin pixels, allowing isolation of those pixels. The eczema region detection uses the CIELAB color model and K-means clustering to extract eczema on the image. The feature extractions have color features composed of RGB, HSV, and YCbCr color models and texture features consisting of contrast, homogeneity, energy, and correlation of GLCM. Then the software will classify the acquired image as healthy, atopic, contact, or nummular using SVM. Next to the testing process, the results are obtained and plotted in a confusion matrix. After analyzing the results, the computed overall accuracy of the system was 83.33%.

Image retrieval is the process of finding images in the database that are similar to the query image by measuring how close the feature values of the query image are to other images. Image retrieval is currently dominated by approaches that combine several different representations or features. The optimal weight of each feature is needed in combining the image features such as color features, texture features, and shape features. In this study, we use a multi-layer perceptron artificial neural network (MLP) method to obtain feature weights automatically and simultaneously look for optimal weights. The color moment is used to find nine color features, Gray Level Co-occurrence Matrix (GLCM) to find four texture features, and Hu Moment to find seven shape features totaling 20 neurons and all of these features become the input layer in our MLP network. Three neurons in output layers become the automatic weight of each feature. These weights are used to combine each feature's role in obtaining the relevant image. Euclidean Distance is used to measure similarity. The average precision values obtained using automatic feature weights are 93.94% for the synthetic dataset, 91.19% for the Coil-100 dataset, and 54.31% for the Wang dataset. These results have an average difference of 5.06% with the target so automatic feature weighting works well. This value is obtained at a hidden layer size of 11 and a learning rate of 0.1. In addition, the use of automatic feature weighting gives more accurate results compared to manual feature weighting.

A technique is proposed to classify regions of interests (ROIs) of digitized mammograms into mass and non-mass regions using texture features and artificial neural network (ANN). Fifty ROIs were extracted from the MIAS MiniMammographic Database, with 25 ROIs containing masses and 25 ROIs containing normal breast tissue only. Twelve texture features were derived from the gray level co-occurrence matrix (GLCM) of each region. The sequential forward selection technique was used to select four significant features from the twelve features. These significant features were used in the ANN to classify the ROI into either mass or non-mass region. By using leave-one-out method on the 50 images using the four significant features, classification accuracy of 86% was achieved for ANN. The test result using the four significant features is better than the full set of twelve features. The proposed method is compared with some existing works and promising results are obtained.

We investigate the reconstruction of low-count positron emission tomography (PET) projection, which is an important, but challenging, task. Using the texture feature extraction method of radiomics, i.e. the gray-level co-occurrence matrix (GLCM), texture features can be extracted from magnetic resonance imaging images with high-spatial resolution. In this work, we propose a kernel reconstruction method combining autocorrelation texture features derived from the GLCM. The new kernel function includes the correlations of both the intensity and texture features from the prior image. By regarding the GLCM as a discrete approximation of a probability density function, the asymptotically gray-level-invariant autocorrelation texture feature is generated, which can maintain the accuracy of texture features extracted from small image regions by reducing the number of quantized image gray levels. A computer simulation shows that the proposed method can effectively reduce the noise in the reconstructed image compared to the maximum likelihood expectation maximum method and improve the image quality and tumor region accuracy compared to the original kernel method for low-count PET reconstruction. A simulation study on clinical patient images also shows that the proposed method can improve the whole image quality and that the reconstruction of a high-uptake lesion is more accurate than that achieved by the original kernel method.

Accurate automatic segmentation of anomalies i.e. ulcers, bleeding regions, polyps, etc. from Wireless Endoscopy Images (WCE) are absolutely essential for proper diagnosis of diseases. Using a single segmentation method for segmenting all these above-mentioned anomalies is a very challenging task due to their different shapes and characteristics. Mostly, existing WCE anomaly segmentation methods are dedicated to only one type of anomaly, but the method proposed in this paper is capable of segmenting four types of anomalies (bleeding regions, small bowel erosions and ulcers, tapeworm and vascular ectasia) from WCE images despite their varied shapes and characteristics. Here we have proposed a novel adaptive statistical method-Convolution Neural Network (CNN) synergism-based two-step segmentation procedure. Initially, we have removed the labeled regions from the acquired WCE images using a polygonal mask and then converted them from the RGB to HSV color space. Here we have chosen HSV color space to carry out the entire segmentation procedure due to its resemblance with the human vision system. In the first step of the segmentation method we extracted the Region of Interest (ROI) from WCE images using statistical measures and by manipulating the intensities of pixels belonging to the ROI and NON-ROI regions accordingly using two adaptive reinforcement constants. The segmented ROI is obtained from this step itself, but it sometimes gives erroneous results. To overcome this shortcoming, we classified the output images of this stage using AlexNet, GoogLeNet, InceptionV3, and SVM trained with Bag-of-Features to obtain accurate results. This stage finally classifies those segmented images into ROI and NON-ROI images. Finally, we obtained a segmentation accuracy of 95.49% using InceptionV3 and also verified the reliability of the method from professional experts.

The development of Multilayer Perceptron (MLP) models for networked learning systems heavily relies on the specific application case study and the accurate parameterization aligned with the chosen computer vision feature extraction models. This study proposes an MLP model for identifying potassium deficiency in cocoa plants. The feature extraction methodology employs object feature extraction that commonly used in computer vision, including Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM), and Hue Saturation Value (HSV) models. These computer vision techniques aid in analyzing leaf characteristics classified into two categories: normal conditions and leaves identified with potassium deficiency. The dataset used in this research comprises two conditions: with a white background and without any specific background. The study evaluates various feature extraction techniques based on MLP parameters, incorporating network learning rates and optimizing solvers. Employing the ROC analysis method throughout the data collection, algorithm development, validation, and analysis phases reveals that the most effective classification performance, reaching up to 93.33% accuracy on the background dataset and 90.00% on the non-background dataset, is achieved using HSV-based color feature extraction with MLP parameters set at an initial learning rate of 10-3 and employing the Adam optimization solver. These outcomes underscore the suitability of HSV color feature extraction for identifying potassium deficiency in cocoa plant leaves. However, optimizing parameters remains crucial to maximize its application in real-time identification systems. Future research should refine these parameters to enhance the model's robustness and efficacy across broader agricultural contexts.

Problem statement: Recently, there has been a huge progress in collection of varied image databases in the form of digital. Most of the users found it difficult to search and retrieve required images in large collections. In order to provide an effective and efficient search engine tool, the system has been implemented. In image retrieval system, there is no methodologies have been considered directly to retrieve the images from databases. Instead of that, various visual features that have been considered indirect to retrieve the images from databases. In this system, one of the visual features such as texture that has been considered indirectly into images to extract the feature of the image. That featured images only have been considered for the retrieval process in order to retrieve exact desired images from the databases. Approach: The aim of this study is to construct an efficient image retrieval tool namely, “Content Based Medical Image Retrieval with Texture Content using Gray Level Co-occurrence Matrix (GLCM) and k-Means Clustering algorithms”. This image retrieval tool is capable of retrieving images based on the texture feature of the image and it takes into account the Pre-processing, feature extraction, Classification and retrieval steps in order to construct an efficient retrieval tool. The main feature of this tool is used of GLCM of the extracting texture pattern of the image and k-means clustering algorithm for image classification in order to improve retrieval efficiency. The proposed image retrieval system consists of three stages i.e., segmentation, texture feature extraction and clustering process. In the segmentation process, preprocessing step to segment the image into blocks is carried out. A reduction in an image region to be processed is carried out in the texture feature extraction process and finally, the extracted image is clustered using the k-means algorithm. The proposed system is employed for domain specific based search engine for medical Images such as CT-Scan, MRI-Scan and X-Ray. Results: For retrieval efficiency calculation, conventional measures namely precision and recall were calculated using 1000 real time medical images (100 in each category) from the MATLAB Workspace database. For selected query images from the MATLAB-Image Processing tool Box-Workspace Database, the proposed tool was tested and the precision and recall results were presented. The result indicates that the tool gives better performance in terms of percentage for all the 1000 real time medical images from which the scalable performance of the system has been proved. Conclusion: This study proposed a model for the Content Based Medical Image Retrieval System by using texture feature in calculating the Gray Level Co Occurrence matrix (GLCM) from which various statistical measures were computed in order to increasing similarities between query image and database images for improving the retrieval performance along with the large scalability of the databases.

Assessment of Neural Network Based Classifiers to Diagnose Focal Liver Lesions Using CT Images

Image analysis-based classification of pigmentation patterns in fish: A case study of pseudo-albinism in Senegalese sole

This research proposed the identification of chicken freshness level based on its color and texture features. Color Features used are the RGB (Red, Green, and Blue) and HSV (Hue, Saturation, Value) channel histogram value. texture features used are GLCM (Grey Level Co-Occurrence Matrix), Gabor kernel, and HOG (Histogram of Oriented Gradients). The freshness level of a chicken meat is categorized into three labels, fresh (0-4 hours after slaughtered), medium-fresh (4-6 hours after slaughtered), and not-fresh (more than 6 hours after slaughtered). The experiments will identify the freshness using several classification methods and different camera resolution and magnification. The highest classification accuracy using SVM (Support Vector Machines) achieves 58,33% with a smartphone camera, 98% with a webcam camera, and 79.1% with a 200 magnification digital microscope. From the experiment results, we can conclude that using webcam camera with normal resolution have better classification accuracy compared with a 200 magnification digital microscope or standard smartphone camera. It is also shown that SVM is superior compared with other methods tested in this experiments which are Decision Tree and Naive Bayes.

South Central Timor (TTS) is one of the districts that has a weaving culture and also produces woven cloth in East Nusa Tenggara. The many types of woven fabric from each TTS tribe makes outsiders and even native TTS people do not recognize the typical TTS woven fabric, therefore we need a system that can help facilitate the community in recognizing the type and motif of woven fabric. In this study, digital image processing is used to identify the type of woven fabric in the TTS district using the HSV color feature extraction method, and the GLCM texture feature, and to measure the similarity of woven fabric using the Euclidean distance method. The image data of the woven fabric used is the image of woven fabric from 3 tribes of TTS district, namely the Amanatun, Amanuban, and Mollo tribes. Identification of woven fabric motifs using the K-fold cross validation test with two stages, namely the training and testing stages. The results of testing variants using 10 fold get an accuracy rate for GLCM texture features of 55%, for HSV color features of 62.5% and a combination of color and texture features of 91.67%.