Title: Microscopic Image Segmentation Based on Swarm Intelligence Optimization Algorithms

Abstract: The World Health Organization suggests the visual examination of stained sputum smear samples as a preliminary and basic diagnostic technique for diagnosing tuberculosis which is the most common infectious disease in the world. Due to the fact that the visual examination of slide samples performed by expert laboratory technicians requires much time and the process is prone to mistake, an accurate diagnosis of disease is provided with computer aided automatic diagnosis methods. In this study, the usage of swarm intelligence algorithms based on entropy information are proposed for detecting the tuberculosis bacilli as an ovelap approach in segmentation of microscopic images. The microscopic images used in the study are taken from smear samples in which the background concentration is low and bacilli concentration is low and high. An optimum threshold value in gray-level microscopic image is determined using the bi-level entropy based Particle Swarm Optimization, Firefly Algorithm, Cuckoo Search Optimization and Flower Pollination Algorithm. The acquired visual results show that the proposed swarm intelligence algorithms are quite successful in segmentation of microscopic images.

Keywords: Mycobacterium tuberculosis; microscopic image segmentation; swarm intelligence algorithms

Başlık: Sürü Zekası Optimizasyon Algoritmaları Tabanlı Mikroskobik Görüntü Segmentasyonu

Özet: Dünya Sağlık Örgütü, dünyanın en yaygın enfeksiyon hastalığı olan tüberküloz hastalığının teşhisi için başlıca teşhis tekniği olarak boyalı balgam yayma örneklerinin görsel incelemesini önermektedir. Uzman laboratuvar teknisyenleri tarafından yayma örneklerinin görsel incelenmesi zaman aldığından ve hataya meyilli olduğundan dolayı bilgisayarla görmeye dayalı otomatik tanı yöntemleri ile hastalığın daha doğru teşhis edilmesi sağlanmaktadır. Bu çalışmada tüberküloz bakterilerinin belirlenmesi amacıyla mikroskobik görüntülerin bölütlenmesinde yeni bir yaklaşım olarak entropi bilgisine dayalı sürü zekası optimizasyon algoritmaları kullanılması önerilmektedir. Çalışmada kullanılan mikroskobik görüntüler, arka plan yoğunluğunun düşük ve basillerin yüksek ve düşük yoğunlukta olduğu yayma örneklerinden alınmıştır. Gri seviyeye çevrilmiş mikroskobik görüntülerde uygun tek bir eşik değeri, iki seviyeli entropi tabanlı Parçacık Sürü Optimizasyonu, Ateş Böceği Algoritması, Guguk Kuşu Algoritması ve Çiçek Tozlaşması Algoritması kullanılarak belirlenmiştir. Elde edilen görsel sonuçlar, önerilen sürü zekası optimizasyon algoritmalarının mikroskobik görüntüleri bölütlemede oldukça başarılı olduğunu göstermektedir.

Anahtar kelimeler: Mycobacterium tuberculosis; mikroskobik görüntü bölütleme; sürü zekası algoritmaları

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