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The non-invasive measurement of mucociliary transit system for CF is required.The automatic circular particles is challenging in Synchrotron X-ray images.A noble method to automatically count the circular shapes is proposed.Robust detection accuracy of 92.7% F-measurement is achieved. Cystic Fibrosis is a genetic disease in which the production of thick sticky mucus compromises the mucociliary transit (MCT) system and causes obstruction of the conducting airways. This results in a cycle of inflammation and infection that dramatically reduces quality of life and causes an early death for most. To directly assess airway health and the effects of potential treatments, synchrotron X-ray imaging techniques have been developed to non-invasively quantify MCT, by visualizing the motion of micron-sized spherical particles deposited into the nasal airways of live mice. Since the level of contrast between the target particles and the background is quite low, and the particles often overlap, most existing methods show a low detection accuracy for the MCT tracking particles in these state-of-the-art PCXI images. This paper proposes a new way to automatically detect the circular shapes of micron-sized particles in these low-contrast X-ray images. The proposed algorithm uses a gradient-directional, sectored ring mask, combined with an edge projection into the ring boundary to identify circular shapes. This new algorithm achieves significantly improved marker particle detection rate, 92.1% precision, 93.9% recall and 92.7% F-measurement, compared to existing methods. It can detect a certain degree of overlapping particles that existing methods struggle to achieve. This algorithm provides automatic MCT particle counting, which significantly reduces the manual labelling process for MCT analysis of living animals.

Recent research has enabled in-vivo examination of mucociliary transit in live airways by analysing the movement patterns of micron-sized markers in high resolution synchrotron X-ray images. However, high levels of false positives and false negatives severely impact the performance of many automated tracking algorithms. This paper proposes an improved approach to track valid mucociliary transit markers using a modified gating region and cost matrix. The proposed method offers a more effective way to associate markers with the correct trackers. Improved visualization methods are also introduced to assist the interpretation of the tracking results. The tracking method has achieved a tracking accuracy of 81.7% track purity and 71.3% track effectiveness. Refereed/Peer-reviewed

Cystic Fibrosis (CF) is a genetic disorder that compromises the respiratory function and the ability of the mucociliary transit (MCT) system. One of the most recent researches introduced a noble method to investigate the progress of the treatment, in which small particles with mostly circular shape injected to the respiratory system and the images were taken using Synchrotron X-ray beam. Since the small particles flow through the respiratory system of the body, the direct observation of MCT measurement will help to understand the progress of the treatment. Identifying the particle is the critical step towards the automatic analysis of the image. However, the objects of interests are usually very small, not perfect circular shape and slightly overlapped from each other with lots of noise due to radiation. This paper proposes a robust and effective detection method of such particles using sectored ring mask (SRM) with gradient descent method. The proposed method extracts strong edges of the particles and the edge line gradients and circle fitting algorithm will filter out invalid edges, resulting in clear particle edge detection. The proposed method has validated through experimental study and presented robust detection rates of 91.9% precision and 89.0%recall. Refereed/Peer-reviewed