臺灣博碩士論文加值系統

本篇論文旨在探討醫學影像上的兩個問題，一是如何幫助醫生收集、存取影像以供教育之用，另一是提供影像傳輸的解決方案以供遠距醫療網之用。
醫學影像不像一般自然影像可以容許失真，影像品質在診斷上是很重要的，因此在傳輸或儲存時，醫學影像通常是使用非失真型壓縮。一般而言，醫學影像是以特定的方式在人體的特定部位取像，若我們鎖定在特定影像並運用這方面的知識在壓縮上，就能達到更高的壓縮比而不會損失醫師診斷所需的必要資訊。為了有效降低傳輸或儲存影像所需的頻寬，我們提出一種分割影像的技術，可用來在胸腔X-光片自動分出醫生感興趣的區域(ROI)，該區域是由肋骨的最外圍所包圍起來的。在區域內使用非失真壓縮，而區域外則使用失真壓縮。
為了幫助放射學家收集醫學影像以供鑑別診斷教學之用，我們提出了一種以內容為基礎的影像存取系統來將視覺上相似的影像群聚在一起，並以之分析類似症狀之間的異同。該系統提供了一個視覺化的介面以方便使用者輸入或查詢其感興趣的區域之影像。我們用自調適類神經網路(Self-Organizing Map)作為群聚的工具，並以快速傅立葉轉換(FFT)擷取影像特徵。實驗結果顯示我們的方法可以有效容忍使用者在框ROI區域時的變異性，並能正確取回所要的影像。
在遠距醫療的領域，由偏遠地區傳輸視訊資料至醫療中心是很重要的。為了傳送視訊，有兩點需要考慮：一是傳輸時要有辦法避免斷線以應付緊急手術。二是為了成本考量，偏遠地區的視訊工作站可能在運算能力與頻寬上都無法與醫療中心相比，因此必須要能動態的調整壓縮運算量以適應不同的運算能力與頻寬。基於以上的考量，我們提出兩種低頻寬視訊壓縮演算法以符合所求。為了避免斷線，我們使用雙通道來傳送視訊，而為了避免浪費資源，雖然兩個通道上傳送的是相同的視訊來源，但卻是用不同的編碼方式，當兩個通道的資料都被收到時，影像品質可以比收到單一通道時還好。
而為了動態調整運算量，我們提出一種演算法讓使用者可以設定所需的運算量而仍能維持相當的影像品質。我們在區塊層(block-level)與框架層(frame-level)分別控制所需的運算量以便達到整體最佳的運算量-失真度(complexity-distortion)比。

This study addresses the problems dealing with medical images - to assist the doctors to collect/retrieve visually similar images for education usage, and to provide image communication solution for telemedicine.
The quality of medical images is crucial in diagnosis. Unlike common natural images, medical images are more specific and could not be distorted during transmission and storage. Normally, medical images are taken under special condition and specific to the special part of human body. If we could utilize the domain knowledge of specific type of medical images, higher compression ratio could be achieved without losing any useful diagnostic information. In order to reduce the cost of storage and transmission bandwidth, we proposed a segmentation method to automatically locate the ROI area in a chest radiograph medical image, which is enclosed by ribs. By utilizing the knowledge of significant edge of ribs, our method can successfully locate the area that is bounded by ribs. The located ROI area is then compressed by the improved SAMAR method, and the non-ROI area is compressed by JPEG.
To help radiologists to collect useful medical images for the tutorial of differential diagnosis, we proposed a content-based image retrieval system to cluster visually similar image patterns, and to mine the similarity and differentiation between similar syndromes. The system uses visual-based user interface to allow the user to enter or query an image by selecting the region of interest (ROI) regions; and uses a neural network method to classify the relationship between the images stored in database. The system will output a set of candidate images that are textural-similar to the query image. In the implementation, we extract the major 2-D FFT coefficients to represent the texture features and uses Kohonen self-organizing network to cluster those extracted FFT coefficients.
In the field of telemedicine, transmitting medical video stream from remote hamlet to clinic is necessary and important. For some consequential operations, we need to build a failure-free communication during some emergency operations. We propose a scalable low bit rate failure-free video-coding algorithm for such transmission. For each video sequence in the failure-free video transmission, two channels are utilized to transmit the same video source. In the receiver end, the video can be reconstructed by any single encoded bitstream. While receiving two encoded bitstreams, the image quality can be improved by combining two bitstreams. In this study, we develop a motion vector refinement technique to improve the image quality.
To meet the criteria of real-time transmission, we proposed a complexity-adaptive fast block-matching algorithm that allows users to terminate processing at any target computational complexity. Two complexity-adaptive implementations, frame level complexity allocation and block level complexity allocation, are developed to approach the global complexity-distortion optimization. In addition, a buffer control strategy was proposed to dynamically adjust the target complexity.

封面
CHAPTER 1 INTRODUCTION
CHAPTER 2 FEATURE EXTRACTION
2.1 INTRODUCTION
2.2 GABOR FILTER
2.3 TEXTURE EXTRACTION BY FOURIER TRANSFORM
2.4 FEATURE EXTRACTION
2.4.1 Defining Coordinate System
2.4.2 Feature Extraction Using FFT
CHAPTER 3 CLUSTERNG BY SELF-ORGANIZING NETWORK
3.1 SELF-ORGANIZING NETWORKS
3.1.1 The SOM lgorithm
3.1.2 Training SOM in Batch Mode
3.2 THE DECISION OF CLUSTERING
3.3 QUERY PROCESSING
3.3.1 Simple Query
3.3.2 Complex Query
3.4 ASSISTIING TEACHING DIFFERENTIAL DIAGNOSIS
3.5 EXPERIMENTAL RESULTS
3.5.1 Query Result
CHAPTER 4 CLASSIFYING THEABNORMAL AREA OF LUNG
4.1 INTRODUCTION
4.2 TOPOGRAPHIC INDEPENDENT COMPONENT ANALYSIS
4.2.1 Lung Cancer Experiments
4.2.2 Normal Tisue Experiments
4.2.3 Comparisons
4.3 SUPPOR VECTOR MACHINE
4.4 CLASSIFICATION RESULTS
CHAPTER 5 KNOWLEDGEBASED COMPRESSION
5.1 DEFINITION OF THE ROI AREA
5.2 EDGE DETECTION FILTER
5.3 LOCATING THE ROI AREA
5.4 ROI-BASED COMPRESSION
5.4.1 System Flow
5.5 LOSSLESS AND LOSSY COMPRESSION
5.5.1 Lossless Compression
5.5.2 Principle of Predictive Based compression
5.5.3 The Sequential Lossless Model of JPEG
5.5.4 Lossless Compression Using Improved SAMAR
5.6 EXPREIMENTAL RESULTS
CHAPTER 6 FAILURE-FREE VIDEO COMMUNICATION
6.1 REFINEMENT OF MOTION VECTORS
6.2 SCALABLE VIDEO CODING SCHEME
6.2.1 Encoding Scheme
6.2.2 Decoding Scheme
6.3 EXPERIMENTAL RESULTS
CHAPTER 7 COMPLEXITY-ADAPTIVE COMPRESSION FOR VIDEO COMMUNICATION
7.1 COMPLEXITY CONTROL
7.1.1 Buffer Control Strategy
7.1.2 Predictive Complexity-Distorition Benefit List
7.2 COMPLEXITY-ADAPTIE BLOCK MATCHING ALGORITHM DESIGN
7.3 EXPERIMENTAL RESULTS
CHAPTER 8 CONCLUSIONS AND FUTURE WORKS
8.1 CONCLUSIONS
8.2 DIRECTION FOR FUTURE WORKS
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