Color Features Extraction and Classification of Digital Images of Erythrocytes Infected by Plasmodium berghei

2019 ◽  
pp. 715-722
Author(s):  
Juan V. Lorenzo-Ginori ◽  
Lyanett Chinea-Valdés ◽  
Yanela IzquierdoTorres ◽  
Rubén Orozco-Morales ◽  
Niurka Mollineda-Diogo ◽  
...  
Keyword(s):  
Plasmodium Berghei ◽  
Digital Images ◽  
Features Extraction ◽  
Color Features

Detection of Yellow Hawkweed (Hieracium pratense) with High Resolution Multispectral Digital Imagery

1995 ◽  
Vol 9 (3) ◽  
pp. 477-483 ◽  
Author(s):  
Hubert W. Carson ◽  
Lawrence W. Lass ◽  
Robert H. Callihan
Keyword(s):  
Supervised Classification ◽  
Digital Images ◽  
Ground Cover ◽  
Unsupervised Classification ◽  
Digital Imagery ◽  
Charge Coupled Device ◽  
Spectral Filters ◽  
A Charge ◽  
Northern Idaho

Yellow hawkweed infests permanent upland pastures and forest meadows in northern Idaho. Conventional surveys to determine infestations of this weed are not practical. A charge coupled device with spectral filters mounted in an airplane was used to obtain digital images (1 m resolution) of flowering yellow hawkweed. Supervised classification of the digital images predicted more area infested by yellow hawkweed than did unsupervised classification. Where yellow hawkweed was the dominant ground cover species, infestations were detectable with high accuracy from digital images. Moderate yellow hawkweed infestation detection was unreliable, and areas having less than 20% yellow hawkweed cover were not detected.

scholarly journals New Segmentation and Feature Extraction Algorithm for the Classification of White Blood Cells in Peripheral Smear Images

2021 ◽  
Author(s):  
Eslam Tavakoli ◽  
Ali Ghaffari ◽  
Seyedeh-Zahra Mousavi Kouzehkanan ◽  
Reshad Hosseini
Keyword(s):  
Blood Cells ◽  
White Blood Cells ◽  
Dice Similarity Coefficient ◽  
Second Phase ◽  
Peripheral Smear ◽  
Color Features ◽  
Svm Model ◽  
Extraction Algorithm ◽  
Processing Techniques

This article addresses a new method for classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it which is located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shape and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.47 %, 92.21 %, and 94.20 %, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets. The obtained results demonstrate that the proposed method is robust, fast, and accurate.

Adaptive Median Filtering Based on Unsupervised Classification of Pixels

2013 ◽  
pp. 273-296 ◽  
Author(s):  
J. K. Mandal ◽  
Somnath Mukhopadhyay
Keyword(s):  
Clustering Algorithm ◽  
Digital Images ◽  
Unsupervised Classification ◽  
Median Filtering ◽  
Adaptive Window ◽  
Novel Approach ◽  
Weighted Median ◽  
Window Selection ◽  
Filtering Approach

This chapter deals with a novel approach which aims at detection and filtering of impulses in digital images through unsupervised classification of pixels. This approach coagulates directional weighted median filtering with unsupervised pixel classification based adaptive window selection toward detection and filtering of impulses in digital images. K-means based clustering algorithm has been utilized to detect the noisy pixels based adaptive window selection to restore the impulses. Adaptive median filtering approach has been proposed to obtain best possible restoration results. Results demonstrating the effectiveness of the proposed technique are provided for numeric intensity values described in terms of feature vectors. Various benchmark digital images are used to show the restoration results in terms of PSNR (dB) and visual effects which conform better restoration of images through proposed technique.

Automated Ripeness Assessment System of Tomatoes Using PCA and SVM Techniques

2014 ◽  
pp. 101-130 ◽  
Author(s):  
Esraa El Hariri ◽  
Nashwa El-Bendary ◽  
Aboul Ella Hassanien ◽  
Amr Badr
Keyword(s):  
Feature Extraction ◽  
Fruits And Vegetables ◽  
Research Work ◽  
Assessment System ◽  
Assessment Process ◽  
Support Vector ◽  
Work Related ◽  
Color Features ◽  
Core Concepts

One of the prime factors in ensuring a consistent marketing of crops is product quality, and the process of determining ripeness stages is a very important issue in the industry of (fruits and vegetables) production, since ripeness is the main quality indicator from the customers' perspective. To ensure optimum yield of high quality products, an objective and accurate ripeness assessment of agricultural crops is important. This chapter discusses the problem of determining different ripeness stages of tomato and presents a content-based image classification approach to automate the ripeness assessment process of tomato via examining and classifying the different ripeness stages as a solution for this problem. It introduces a survey about resent research work related to monitoring and classification of maturity stages for fruits/vegetables and provides the core concepts of color features, SVM, and PCA algorithms. Then it describes the proposed approach for solving the problem of determining different ripeness stages of tomatoes. The proposed approach consists of three phases, namely pre-processing, feature extraction, and classification phase. The classification process depends totally on color features (colored histogram and color moments), since the surface color of a tomato is the most important characteristic to observe ripeness. This approach uses Principal Components Analysis (PCA) and Support Vector Machine (SVM) algorithms for feature extraction and classification, respectively.

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