A DIAMOND method of inducing classification rules for biological data

Identifying the classification rules for patients, based on a given dataset, is an important role in medical tasks. For example, the rules for estimating the likelihood of survival for patients undergoing breast cancer surgery are critical in treatment planning. Many well-known classification methods (as decision tree methods and hyper-plane methods) assume that classes can be separated by a linear function. However, these methods suffer when the boundaries between the classes are non-linear. This study presents a novel method, called DIAMOND, to induce classification rules from datasets containing non-linear interactions between the input data and the classes to be predicted. Given a set of objects with some classes, DIAMOND separates the objects into different cubes, and assigns each cube to a class. Via the unions of these cubes, DIAMOND uses mixed-integer programs to induce classification rules with better rates of accuracy, support and compact. This study uses three practical datasets (Iris flower, HSV patients, and breast cancer patients) to illustrate the advantages of DIAMOND over some current methods.     

正常国人心表面积256层动脉期增强CT测量

目的： 通过手动分割重建心多层螺旋CT（ MSCT）动脉期图像,定量测量正常国人心表面积并建立不同 年龄范围心表面积数据库。方法： 采用回顾性横断面研究方法,自2016 年1 月～ 2019 年12 月在本院行256 层心 增强CT检查的受检者中,筛选无心、大血管病变,且心各房室显示优良者,按照不同年龄范围分为5 组。将正 常心动脉期薄层数据导入3D Slicer 软件,手动逐层勾画各心房、心室进行三维体数据的图像分割标注,重建全心 及各心房、心室模型,并通过Marching Cubes 算法分别测量。观测不同年龄阶段心表面积、左心房及左心室表面积、 右心房及右心室表面积。结果： 正常受检者心表面积为（598.45±49.97）cm2,其中左心房表面积（154.46±34.33） cm2,左心室表面积（220.06±38.72）cm2,右心房表面积（179.07±36.61）cm2,右心室表面积（190.06±36.61） cm2。男性、女性心表面积分别为（610.50±42.75）cm2、（580.86±47.13）cm2。随着年龄增长,心表面积及各心房、 心室表面积均有增大。结论：基于手动分割重建心多层螺旋CT技术可获得正常国人心表面积测量值,为心脏疾 病的诊断和治疗提供依据。     

Efficient computation of enclosed volume and surface area from the same triangulated surface representation

We demonstrate that the volume enclosed by triangulated surfaces can be computed efficiently in the same elegant way the volume enclosed by digital surfaces can be computed by digital surface integration. Although digital surfaces are effective and efficient for visualization and volume measurement, their drawback is that surface area measurements derived from them are inaccurate. On the other hand, triangulated surfaces give more accurate surface area measurements, but volume measurements and visualization are less efficient. Our data structure (called t-shell) for representing triangulated digital surfaces retains advantages and overcomes difficulties of both the digital and the triangulated surfaces. We create a lookup table with area and volume contributions for each of the 256 Marching Cubes configurations. When scanning the shell (e.g., while creating it), the surface area and volume are incrementally computed by using the lookup table and the current x co-ordinate, where the sign of the x component of the triangle normal indicates the sign of the volume contribution. We have computed surface area and volume for digitized mathematical phantoms, physical phantoms, and real objects. The experiments show that triangulated surface area is more accurate, triangulated volume follows digital volume closely, and that the values get closer to the true value with decreasing voxel size.