Lu Difei (1972-，Ph.D.)， a professor of Zhejiang Institute of Mechanical and Electrical Engineering. Researchinterests include computer graphics and image technology， medical imageprocessing and industrial internet technology， etc.Several papers such as”Iterative mesh transformation for 3D segmentation of livers with cancers in CTimages” ”Animating by example” ”A fastt rapeziums-based method for soft shadow volumes” and a book Samplesbased animation creating and application have been published.Several achievements have won provincial and municipal scientificand technological progress awards， nearly30 academic papers have been published，and more than 10 patents and sof tware copyrights have been obtained.
目錄：
Part One (Chapters 1～4)
Chapter 1Animating by example3
1.1The significance of the animating by example method3
1.2Surveys of related existing research and overview of our method6
1.2.1Surveys of related existing research6
1.2.2Overview of our method16
1.3The main steps of the core algorithm18
1.3.1Sketch based mapping18
1.3.2Differential mean value coordinates for mesh deformation22
1.3.3Stitching and smoothing meshes25
1.3.4Interpolating between key frames31
1.3.5Mapping 2D animations to 3D characters32
1.3.6Solving the minimization problem for smoothing mesh35
1.4Results of the method38
1.5Conclusions of this chapter39
Chapter 2A new method of interactive markerdriven freeform mesh
deformation43
2.1The related works of free form mesh deformation43
2.2Overview of the new method of freeform mesh deformation44
2.3The main steps of interactive markerdriven freeform mesh deformation45
2.3.1The algorithm of shape deformation 45
2.3.2The algorithm of mesh smoothing49
2.3.3Interpolating between two key frames51
2.4Conclusions of this chapter52
Chapter 3Asrigidaspossible deformation clone54
3.1The related works of deformation clone54
3.2Overview of the method60
3.3The algorithm of deformation clone61
3.4The results and conclusions of the method64
3.4.1The results of the method proposed in this chapter64
3.4.2The conclusions of the method proposed in this
chapter65
Chapter 4A fast trapeziumsbased method for soft shadow volumes67
4.1Previous works of soft shadow volumes67
4.2Obtaining soft shadows with ray tracing71
4.2.1Finding out all global potential silhouette edges71
4.2.2Getting local potential silhouette edges and exact silhouette edges72
4.2.3Projecting， modifying and splitting silhouette edges 73
4.2.4Constructing trapeziums to determine the visibility oflight source76
4.3Results and conclusions of the algorithm77
Part Two (Chapters 5 and 6)
Chapter 5
Interactive mesh segmentation and contour optimization forliver & tumors 83
5.1The related works of liver and tumors segmentation83
5.2Outline of the approach91
5.3Main steps of the approach92
5.3.1Finding chest bones92
5.3.2Constructing chest mesh92
5.3.3Constructing liver mesh102
5.3.4Extended intelligent scissors104
5.3.5Liver segmentation scheme109
5.4Experiments and results112
5.5Discussion and conclusions of this chapter117
Chapter 6Design and development of smart PACS based on 3D intelligent scissors121
6.1Design goals121
6.2Feasibility analysis122
6.3Main development content122
6.3.1Optimization of 3D intelligent scissors algorithm122
6.3.2Development of smart PACS system122
6.3.3System architecture 123
6.4System development and implementation124
6.4.1System modules124
6.4.2Interface and tips of smart PACS147
6.5Video of academic results created by smart PACS156
Appendix 1: Sparse matrix algorithms and software166
Appendix 2: Solving the minimization problem169
Appendix 3: Detailed evaluation results of 40 cases173
Appendix 4: Detailed evaluation results of 10 cases downloaded from MICCAI database176
Appendix 5: Introduction to DICOM177
Appendix 6: Project file format of smart PACS179
References180
內容試閱：
Nowadays cancer deaths have ranked first among all causes of death in China. With the development of CT imaging technology， due to its relatively low cost and effectiveness and reliability， it has gradually become an extremely important means for cancer diagnosis， treatment and effect evaluation. Many vital organs of the human body are inside the abdominal cavity， and processing & analyzing the CT images of the abdominal cavity of cancer patients are very important for the patients later treatment. However， due to the difference between CT images and traditional imaging techniques， CT images inevitably have the characteristics of blur and unevenness. The application of CT images mainly depends on the radiologists understanding and interpretation of the images. The doctors subjectivity and experience have a great influence on analyzing the CT images. Therefore， the use of computer technology to process CT images is becoming more and more significant. Among them， CT image segmentation technology is the basis for the further processing of CT images， as well as the basis for medical measures such as pathological analysis and surgical plan formulation. It has an irreplaceable role in imaging medicine and is a difficult point in current medical image processing.
The current domestic Internet medical imaging industry is still in its infancy， and most medical software companies are limited to the field of medical information. Now with the advancement of artificial intelligence technology， the accuracy of diagnosis is continuously improved. Quantitative analysis products of tumor imaging can be used in hospital clinics， physical examination centers and other medical testing institutions to make accurate diagnosis， predict the development of the disease， guide medication and help patients in remote medical diagnosis. This is a new trend in the development of medical imaging diagnosis and has an equally important position as the mainstream medical equipment. At the same time， its products can also be used in pharmaceutical research and development institutions to achieve accurate and quantitative determination of drug efficacy. On the other hand， through the application of big data analysis， the time in the drug development phase is greatly shortened.
With the continuous using of new domestic imaging equipment in primary hospitals， the post image processing technology， especially the highend image quantitative analysis technology， is relatively backward， lacking corresponding softwares and clinical experience， which greatly limits the clinical applications of these advanced imaging equipments. The provision and establishment of image analysis laboratories for hospitals at all levels are not only difficult in terms of financial and material resources， but more importantly， they are technically difficult to achieve. The PACS (picture archiving and communication systems) based on threedimensional intelligent scissors proposed in this book provides a technically feasible solution for this problem. With the help of the core content of this book， three main functions have been implemented:
（1） providing patients with professional cancer curative effect evaluation， including image reading report， quantitative evaluation report， and treatment plan medical consulting services;
（2） undertaking clinical drug trials for pharmaceutical companies， and providing medical services for pharmaceutical companies. Research institutions can provide a multicenter cancer drug trial efficacy evaluation service;
（3） providing medical insurance institutions with drug efficacy and medical cost analysis based on big data.
The core algorithm of this book is an important basis for tumor efficacy evaluation standards， such as WHO (World Health Organization) and RECIST (Response Evaluation Criteria in Solid Tumors)， which can provide a quantitative basis and tools for drug trials. However， the real integration of quantitative imaging technology to promote clinical application has just begun in the world. The main difficulty is that a detailed tumor tracking and efficacy evaluation report requires the radiologist to combine the diagnosis measurement of multiple tumors at multiple time points. Data analysis and evaluation greatly decrease the workload of imaging doctors. The computeraided measurement and evaluation tools proposed in this book are the basis of computeraided quantitative image analysis and evaluation technology， and are provided for the realization of precise tumor treatment. An imagingbased auxiliary tool will truly promote the clinical practice of accurate efficacy evaluation.
The core algorithm of this book can be provided for liver segmentation， processing the CT scan data of the liver， and providing automatic liver image segmentation processing for liver surgery planning.
This book is organized as follows: Chapter one proposes an exampledriven synthesis approach for creating 3D (threedimensional) animation of a target character; Chapter two proposes a new scheme for markerdriven free form global mesh deformation without manually establishing a skeleton or freeform deformation domain beforehand; Chapter three presents a method which transfers the deformation of a source mesh onto a different target mesh while minimizing the distortion of the target mesh; Chapter four proposes an algorithm which produces high quality shadow; Chapter five proposes a liver segmenting method based on 3D intelligent scissors; Chapter six designs a framework of smart PACS. The first four chapters are the foundation and related work of the last two chapters.
This book was supported by the research project LGG19H180001， a basic public welfare project in Zhejiang Province. Thanks to Professor Wenli Cai of Harvard Medical School for his help. The main contribution of this book lies in the application of graphics technology to the analysis and processing of medical images， which has greater application value.
Due to the limited level of the author of this book， there may be some improprieties in the book， please correct.
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