Authors:
Ryosuke Iida
1
;
Kazuki Hashimoto
1
;
Kouich Hirata
1
;
Kimiko Matsuoka
2
and
Shigeki Yokoyama
3
Affiliations:
1
Kyushu Institute of Technology, Kawazu 680-4, Iizuka 820-8502, Japan
;
2
Osaka General Medical Center, Bandaihigashi 3-1-56, Sumiyoshi, Ohsaka 558-8558, Japan
;
3
KD-ICONS, Ohmoriminami 4-6-15-304, Ohta, Tokyo 143-0013, Japan
Keyword(s):
Gram Stain, Gram Stained Smears Images, Gram Types, Gram Positive Cocci, Gram Positive Bacilli, Gram Negative Cocci, Gram Negative Bacilli.
Abstract:
In this paper, we develop the detection system of Gram types determined by stained colors and stained shapes for bacteria from Gram stained smears images. Here, we call four types of bacteria, that is, Gram positive cocci (GPC), Gram positive bacilli (GPB), Gram negative cocci (GNC) and Gram negative bacilli (GPB) Gram types, and then add to two types as Gram positive unknown (GPU), and Gram positive unknown (GNU). The system first infers the candidate regions of bacteria by using image processing. Next, it constructs a classifier dividing the candidate regions into Gram types by using SVM (support vetcor machine) and DNN (deep neural network). Finally, it detects the occurrences of Gram types in a newly input image and retrieves Gram stained smears images similar as the input image such that the occurrence ratio for the Gram types is similar.
