3D visualization methods of human bone and organs were applied to diagnosis as long as 100 years ago [

1]. Normally, 3D medical imaging system aims to provide both quantitative and qualitative information for diagnosis. 3D visualization system can be divided into 4 operations: (1) preprocessing that deals with the volume of interest and features extraction; (2) visualization processes generate 3D object from 2D images; (3) manipulation explains the geometry of object that can be distorted and deformed; (4) analysis that deals with methods of quantify 3D object [

2]. For medical volumetric rendering where 2D binary images (CT-volume) are feed to construct 3D object, collision detection algorithm is used to manipulate the intersecting fragments and generate triangulated mesh models (see

Figure 1a). The doctor can take this advantage of training by observing the demonstration of context of 3D manipulation of bone fragments and the resulting CT images [

3]. More works of 3D visualization such as texture mapping and semi-automatic image segmentation. Texture mapping technique constructs 3D object by interpreting the spatial relationships of 2D binary images and generates 3D visualization of perspective-mapped from each image layer [

4]. Whereas, semi-automatic image segmentation allows the doctor to make the segmentation of subject by the area of interest [

5]. Therefore, the major drawback of these methods is the computational cost of real time resampling for making texture in 3D object reconstruction process [

4], and infeasibility for each individual segmentation [

5] (see

Figure 1b,c). The alternative choice for 3D volumetric rendering is marching cubes. The marching cubes method keeps the coordinates conveyed by traversing the outline of 2D binary shape and marches them to construct 3D object [

6,

7,

8,

9,

10]. The algorithm is based on the configuration of 15 fundamental cubes (see

Section 2).

Figure 2 shows 3D human head model constructed by marching cubes method; the method used 150 slides of 2D image as input source.

It seems like the marching cubes can reduce the computational time used for resampling in 3D reconstruction, but the problem still remains in observing the qualitative information of 3D surface constructed using marching cubes. One major problem of marching cubes is the unused voxels which can be generated during parsing the coordinates and the intensity values of 2D images, these unused voxels affect for the smoothness of 3D surface (the detail of the surface smoothness will be explained in

Section 4 and

Section 6). To overcome this drawback, this paper introduces histogram pyramids with marching cubes method for 3D medical volumetric rendering. The histogram pyramids organize the image entries to form the voxel related to the index values, the traversal of histogram pyramids is used to construct the point list that will be later used for generating the voxel. The organization of paper consists of (1) Introduction, (2) The brief concept of marching cubes method, (3) Reading the intensity value of CT images for 3D rendering, (4) Histogram pyramids, (5) Implementation, (6) Results, and (7) Conclusion and future works.