Author: Xue Wang, Jing Zhang, and C.-C. Jay Kuo

The importance of this research work lies in related morphological characters evaluation for mitochondria objects after accurate object extraction. Studies have shown that the fusion-fission dynamics of mitochondria is involved in many cellular processes, including maintenance of adenosine triphosphate (ATP) levels, redox signaling, oxidative stress generation, and cell death [1-4]. Therefore, mitochondrial morphology can reveal the physiological or pathological status of mitochondria and in a typical analysis, and researchers manually label the mitochondria morphological structures into several subtypes, such as fragmented, networked, and swollen structures [5]. However, although there exist a number of algorithms for mitochondria segmentation [6-8], they require careful manual tuning and optimization while the resultant segmented mitochondria objects are still not correctly classified into standardized morphological subtypes. The challenge is that the gray-level fluorescent intensity is the only clue to segment background from foreground mitochondrial objects.

To overcome the challenge, our work aims at applying computer vision techniques to achieve accurate segmentation based on texture feature extraction for morphological characters. A 2-stage segmentation system (as shown in Fig. 1) has been built to realize automated mitochondria segmentation.

In the Stage I where machine learning classifiers are trained for initial segmentation, the key to the success of this part is that the image signal can be transformed and represented by a linear combination of a subset of extracted texture features, and data grouping methods are applied to enhance the accuracy of classifiers. Our work shows that learning-based approaches fit our problem as they can overcome the existing challenges.

In the Stage II of mitochondria centerline extraction, the cost function is designed based on the human learning/labeling experience to judge the occurrence of connection for each pair of [...]