With the rapid development and wide application of medical imaging technology, explosive volumes of medical
image data are produced every day all over the world. As such, it becomes increasingly challenging to manage
and utilize such data effectively and efficiently. In particular, content-based medical image retrieval has been
intensively researched in the past decade or so.
In this work, we propose a novel approach to content-based medical image retrieval utilizing the co-occurrence
of both the texture and the shape features in contrast to most previous algorithms that use purely the texture
or the shape feature. Specifically, we propose a novel form of representation for the co-occurrence of the texture
and the shape features in an image, i.e., the gray level and edge direction co-occurrence matrix (GLEDCOM).
Based on GLEDCOM, we define eleven features forming a feature vector that is used to measure the similarity
between images. As a result, it consistently yields outstanding performance on both images rich in texture (e.g.,
image of brain) and images with dominant smooth regions and sharp edges (e.g., image of bladder).
As demonstrated by experiments, the mean precision of retrieval with GLEDCOM algorithm outperforms a
set of representative algorithms including the gray level co-occurrence matrix (GLCM) based, the Hu's seven
moment invariants (HSMI) based, the uniformity estimation method (UEM) based and the the modified Zernike
moments (MZM) based algorithms by 10%-20%.
A progressive lossless 3D geometry encoder using a hierarchical vertex set split method is presented in this work. Compared with prior art, the proposed coder has significantly better rate-distortion (R-D) performance at low bit-rates and provides visually pleasant intermediate meshes at all bit-rates. Given a 3D mesh, all its 3D vertices form an initial vertex set, which is split into several child vertex sets using the well-known Generalized Lloyd Algorithm (GLA). Each newly generated vertex set that contains more than one vertex is iteratively split so as to form a hierarchical structure. During the process of hierarchical vertex set split, a representative is calculated for each newly generated vertex set. Then, the representatives of all existing vertex sets form an approximation to the original 3D geometry. For each vertex set split, the number of child vertex sets is arithmetic encoded, and the offsets of the child representatives from their parent representative are sorted, quantized and arithmetic encoded. If a finer resolution is required for a vertex set containing only one vertex, the rectangloid cell containing that vertex can be further subdivided and coded iteratively. Experimental results are provided to demonstrate the superior performance of the proposed geometry coder.
A progressive 3D mesh coding scheme using the octree-based space
partitioning is proposed in this work, which achieves better coding
efficiency than the state-of-the-art kd-tree-based codec. Given a 3D mesh, the quantized 3D vertices are first partitioned into an octree structure. The octree is then traversed from the root and gradually to the leaves. During the traversal, each 3D cell in the tree front is subdivided into eight child cells through three orthogonal cell bi-partitionings. For each cell subdivision, the information of nonempty child cells is encoded. To encode the information, two approaches (i.e. the bit-pattern coding approach and the nonempty-child-cell-tuple coding approach) are implemented and compared. In addition to the geometry coding, the local connectivity update associated with each cell subdivision is also encoded. Furthermore, selective cell subdivision is performed in the tree front to provide better rate-distortion performance, especially at low bitrates. It is shown in experimental results that the geometry coding cost is around 4.2 bits per vertex (bpv) for 8-bit coordinate quantization and 14.3 bpv for 12-bit coordinate quantization, and the connectivity coding cost is 3.3 bpv on the average.
A progressive 3D geometry coding scheme based on the octree structure is proposed in this work, which achieves better coding efficiency than the state-of-the-art geometric codec known as the kd-tree-based codec. Given a 3D mesh, the quantized 3D vertices are first partitioned into an octree structure. The octree is then traversed from the root and gradually to the leaves and, during the traversal, each 3D cell in the tree front is subdivided along three orthogonal directions. For each cell subdivision, the order of subdivision directions is adaptively chosen, the neighborhood-prediction is used and the vertex number distribution is efficiently encoded. The final output bit stream contains relevant information associated with each cell subdivision. It is shown in experimental results that the coding cost is around 15.6 bits per vertex (bpv) for 12-bit coordinate quantization and 5.6 bpv for 8-bit coordinate quantization on average.
Among progressive 3D mesh compression algorithms, the kd-tree-based algorithm proposed by Gandoin and Devillers is one of the state-of-the-art algorithms. Based on the observation that this geometry coder has a large amount of overhead at high kd-tree levels, we propose an octree-based geometry coder that demands a less amount of coding bits at high octree levels by applying selective cell subdivision at high tree levels, leading to a better rate-distortion performance for the low bit rate coding. Experimental results show that, compared with the kd-tree-based coder, the proposed 3D geometry coder performs better for an expanded tree of a level less than or equal to 8 but slightly worse for the full tree expansion with 12-bit quantization.
A new approach to estimate the surface curvatures from 3D triangular mesh surfaces with Gaussian curvature's geometry interpretation is proposed in this work. Unlike previous work, the proposed method does not use local surface fitting, partial derivative computation, or oriented normal vector recovery. Instead, the Gaussian curvature is estimated at a vertex as the area of its small neighborhood under the Gaussian map divided by the area of that neighborhood. The proposed approach can handle vertices with the zero Gaussian curvature uniformly without localizing them as a separate process. The performance is further improved with the local Bezier curve approximation and subdivision. The effectiveness of the proposed approach for meshes with a large range of coarseness is demonstrated by experiments. The application of the proposed method to 3D surface segmentation and 3D mesh feature extraction is also discussed.