An Interest-based Ordering Scheme (IOS) for fuzzy morphology on White-Blood-Cell (WBC) image segmentation is proposed to improve accuracy of segmentation. The proposed method shows a high accuracy in segmenting both high- and low-density nuclei. Further, its running time is low, so it can be used for real applications. To evaluate the performance of the proposed method, 100 WBC images and 10 leukemia images are used, and the experimental results show that the proposed IOS segments a nucleus in WBC images 3.99% more accurately on average than the Lexicographical Ordering Scheme (LOS) does and 5.29% more accurately on average than the combined Fuzzy Clustering and Binary Morphology (FCBM) method does. The proposal method segments a cytoplasm 20.72% more accurately on average than the FCBM method. The WBC image segmentation is a part of WBC classification in an automatic cancer-diagnosis application that is being developed. In addition, the proposed method can be used to segment any images that focus on the important color of an object of interest.

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An efficient network for super-resolution, which we refer to as inception learning super-resolution (ILSR), is proposed. We adopt the inception module from GoogLeNet to exploit multiple features from low-resolution images, yet maintain fast training steps. The proposed ILSR network demonstrates low computation time and fast convergence during the training process. It is divided into three parts: feature extraction, mapping, and reconstruction. In feature extraction, we apply the inception module followed by dimensional reduction. Then, we map features using a simple convolutional layer. Finally, we reconstruct the high-resolution component using the inception module and a 1×1 convolutional layer. Experimental results demonstrate that the proposed network can construct sharp edges and clean textures, and reduce computation time by up to three orders of magnitude compared to state-of-the-art methods.

A DNA similarity matching using fuzzy inference system is proposed to measure a similarity between human STR (Short Tandem Repeat) based DNA (Deoxyribonucleic Acid) profiles. Moreover, various defuzzification methods are also tested to observe their behavior on different DNA data characteristics. Experiment on real human STR based DNA profile data shows that the proposed DNA similarity matching produces more realistic similarity values compared to those of the conventional one. Experiment on various defuzzification methods on DNA similarity matching shows that Sugeno defuzzification method is more suitable than those of other defuzzification methods.

Dental classification for periapical radiograph based on multiple fuzzy attribute is proposed, where each tooth is analyzed based on multiple criteria such as area/perimeter ratio and width/height ratio. A classification method on special type of dental image called periapical radiograph is studied and classification is done without speculative classification (in case of ambiguous object), therefore an accurate and assistive result can be obtained due to its capability to handle ambiguous tooth. Experiment results on 78 periapical dental radiographs from University of Indonesia indicates 82.51% total classification accuracy and 84.29% average classification rate per input radiograph. The proposed classification method is planned to be implemented as a submodule for an under developing dental based personal identification system.

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