{'fraction_non_alphanumeric': 0.03548153511947864, 'fraction_numerical': 0.012309920347574221, 'mean_word_length': 5.427906976744186, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Automatic IOCT lumen segmentation using Wavelet and Mathematical Morphology

Coronary diseases cause approximately 1 death per minute in USA. Intravascular Optical Coherence Tomography (IOCT) is one of the most used Medical Imaging Modality for coronary investigations. However, segmentation is important to obtain objective information. Hence, better diagnostics and evaluations are provided. Many methods in the literature have not been applied in IOCT segmentation. Therefore, to improve segmentation accuracy, offering more choices to developers, new and alternative approaches are necessary. We present an automatic lumen segmentation approach, based on Wavelet and Mathematical Morphology. The methodology can be summarized as following. First, after preparing the image in a typical preprocessing stage, wavelet is performed to discriminate the tissue from the rest of the image. Next, a moving window Otsu binarization process is carried out. Finally, a Mathematical Morphology takes place to correct, polish and estimate the information previously provided, so that an accurate binary version of the lumen shape, and its contour could be obtained. The evaluation was carried with 130 images from human and pig coronaries, and rabbit's iliac arteries. The high accuracy was demonstrated with values of True Positive (TP(%)) = 99.27±1.29, False Positive (FP(%)) = 3.43±1.51.