Yerel İkili Örnek Kullanarak Üç Lallemantia Fisch. & C.A. Mey. Türünün Kromozom Sınıflandırmasına Farklı Bir Yaklaşım

Abstract

Geleneksel karyotip çalışmaları, bitki sistematiğinde türlerin konumlarını değerlendirmek için yaygın olarak kullanılmaktadır. Ancak çalışmalar bazen sistematik sorunları çözememektedir. Son yıllarda bilgisayar tabanlı sistemler taksonomik problemlerin çözümüne katkı sağlamada önem kazanmıştır. Bu çalışmanın amacı, mitozdaki kromozom görüntülerine dayalı yerel ikili örüntü doku operatörünü kullanarak üç Lallemantia türünü belirlemektir. Yerel ikili örüntü, yerel görüntü modellerini tanımlamak için en güçlü ve kolay uygulanabilir araçlardan biridir. Bu çalışmada mitozun metafaz evresindeki 641 hücrenin mikrofotoğrafları kullanılmıştır. Yerel ikili örüntü, ön işleme, özellik çıkarma, özellik seçme ve sınıflandırmayı içerir. Sınıflandırma aşamasında DT, LD, SVM, KNN, BT ve SNN kullanılmıştır. Bu çalışma, başarı oranı yüzde yüz olduğu için en iyi sınıflandırıcının SNN olduğunu buldu. Ayrıca, üç tür arasındaki benzerliği ölçmek için bir dendrogram oluşturulmuştur. Sonuç olarak, LBP, kromozom görüntülerini kullanarak bitkileri sınıflandırmak için bir araç olarak kabul edilebilir.

Keywords

• Bitki sınıflandırması
• kromozom
• görüntü işleme
• Lallemantia
• yerel ikili örüntü

A Different Approach To The Chromosome Classification of Three Lallemantia Fisch. & C.A. Mey. Species By Using Local Binary Pattern (LBP)

Abstract

The traditional karyotype studies are widely used in plant systematics to evaluate the positions of species. However, studies sometimes can not solve systematic problems. In recent years, computer-based systems have gained importance in contributing to the solution of the taxonomical problems. The aim of this study was to identify three Lallemantia species by using the LBP (local binary pattern) texture operator based on chromosome images in mitosis. LBP is the one of the most powerful and easily applicable tool for identifying local image patterns. In this study, microphotographs of 641 cells in the metaphase stage of mitosis were used. The LBP involves preprocessing, feature extraction, feature selection, and classification. Decision tree (DT), linear discriminant (LD), support vector machine (SVM), K-nearest neighbor (KNN), bagged tree (BT) and ensemble subspace nearest neighbor (SNN) were used in the classification stage. This study found that the best acting classifier was SNN because achievement rate was one hundred percent. Also, a dendrogram was formed to measure the similarity among the three species. As a result, LBP can be accepted as a tool for classifying plants by using chromosome images.

Keywords

• Chromosome
• image processing
• Lallemantia
• local binary pattern
• plant classification

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