The ability to predict the local segmentation SD could eliminate the need of repeated segmentations of a 2-D transverse image to obtain the local segmentation standard deviation, thereby making clinical trials less laborious and saving time.
Presumably, a clearly articulated collective word intuition for Chinese speakers would be the ideal word segmentation standard for Chinese language processing and would also make modeling and evaluation of Chinese word segmentation explicit and straightforward.
In computational and Chinese language processing, the difficulty in capturing collective word intuition leads to the difficulty in consensus in articulating consensus word segmentation standard (Huang et al. 1996; Liu et al. 刘源等 1994) and in achieving optimal results in word segmentation (Huang and Xue 2012; Huang and Xue 2015; Huang and Zhao 黄昌宁, 赵海 2007).
The possible link between collective word intuition and word segmentation standard, as well as the fact that previous studies failed to recruit enough subjects for valid results lead to our proposal to use the NLP technique of crowdsourcing to tackle this issue.
Figure 5d f shows the binary image that represents the final segmentation of the standard KM, the standard FCM, and the proposed DHFCM segmentation algorithm, respectively.
The software automatically divided the left ventricle into six segments based on standard segmentation model used in human patients [ 17].
The method is evaluated objectively against a gold standard segmentation and transcription, as well as subjectively through building and testing speech synthesis systems from the retrieved data.
Let us mention that for such a region, K-Means may provide comparable, and may be even, better segmentation results than standard HMC and MS- HMC.
Therefore, it is difficult to apply standard segmentation algorithms, such as region growing [7], since fiber borders may be partly unclear and unwanted flooding may occur.
The use of CT contrast in combined imaging has been the subject of debate among PET/CT users [21,22], the reason being that standard, segmentation, and bi-linear scaling methods of CT-AC [23,24] fail to adequately scale contrast-enhanced tissues or filled organs, thus giving rise to artifacts and bias.
