Detection of Optimal Puncture Position in OVUM Images for Artificial Insemination This paper aims to determine the optimal puncture position of ovum by evaluating rupture membrane of cytoplasm. We employed 139 ovum images on the PiezoICSI (Intracytoplasmic sperm injection). In it, grayscale images before puncture and their actual puncture position were obtained from the movie file (Rupture:31, No Rupture:108), and Local Binary Pattern (LBP) feature is calculated at analysis area around the puncture position. LBP feature dimensions are reduced, and data are classified by hierarchical clustering method using feature of three dimensions. As a result, the data classified into two clusters (Clusters A and B). Cluster A has 7 Ruptures and 50 No Ruptures, Cluster B has 24 Ruptures and 58 No Ruptures. Then, the sensitivity is 0.77. Therefore, it is possible to evaluate rupture membrane of cytoplasm from shape feature of membrane. The optimal puncture position could be determined by the features.

SVMEnabled Intelligent Genetic Algorithmic Model for Realizing Efficient Universal Feature Selection in Breast Cyst Image Acquired via Ultrasound Sensing Systems In recent years, there are several costeffective intelligent sensing systems such as ultrasound imaging systems for visualizing the internal body structures of the body. Further, such intelligent sensing systems such as ultrasound systems have been deployed by medical doctors around the globe for efficient detection of several diseases and disorders in the human body. Even though the ultrasound sensing system is a useful tool for obtaining the imagery of various body parts, there is always a possibility of inconsistencies in these images due to the variation in the settings of the system parameters. Therefore, in order to overcome such issues, this research devises an SVMenabled intelligent genetic algorithmic model for choosing the universal features with four distinct settings of the parameters. Subsequently, the distinguishing characteristics of these features are assessed utilizing the SorensenDice coefficient, Ttest, and Pearsonxe2x80x99s R measure. It is apparent from the results of the SVMenabled intelligent genetic algorithmic model that this approach aids in the effectual selection of universal features for the breast cyst images. In addition, this approach also accomplishes superior accuracy in the classification of the ultrasound image for four distinct settings of the parameters.