An automatic seeded region growing algorithmbased on contour processing for grayscale images

In this paper, an automatic seeded region growing algorithm based on contour processing for grayscale images is proposed. In which, the grayscale image is first read. After that, the image is subjected to contour processing. The contour image is then segmented using simple SRG by assigning labels to each pixel in a specific segment. The segmented image is then compared to original input image to find the suitable seed in the segment depending on the average brightness of pixels forming the segment. Then these seeds are used again to generate segments using SRG algorithm. The proposed algorithm is shown that it is more accurate in finding number of segments than traditional segmentation algorithms

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images

New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced datasets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present thesis introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images.Comment: 218 pages, 58 figures, PhD thesis, Department of Mechanical Engineering, Karlsruhe Institute of Technology, published online with KITopen (License: CC BY-SA 3.0, http://dx.doi.org/10.5445/IR/1000057821

IMPROVING DAILY CLINICAL PRACTICE WITH ABDOMINAL PATIENT SPECIFIC 3D MODELS

This thesis proposes methods and procedures to proficiently introduce patient 3D models in the daily clinical practice for diagnosis and treatment of abdominal diseases. The objective of the work consists in providing and visualizing quantitative geometrical and topological information on the anatomy of interest, and to develop systems that allow to improve radiology and surgery. The 3D visualization drastically simplifies the interpretation process of medical images and provides benefits both in diagnosing and in surgical planning phases. Further advantages can be introduced registering virtual pre-operative information (3D models) with real intra-operative information (patient and surgical instruments). The surgeon can use mixed-reality systems that allow him/her to see covered structures before reaching them, surgical navigators for see the scene (anatomy and instruments) from different point of view and smart mechatronics devices, which, knowing the anatomy, assist him/her in an active way. All these aspects are useful in terms of safety, efficiency and financial resources for the physicians, for the patient and for the sanitary system too. The entire process, from volumetric radiological images acquisition up to the use of 3D anatomical models inside the surgical room, has been studied and specific applications have been developed. A segmentation procedure has been designed taking into account acquisition protocols commonly used in radiological departments, and a software tool, that allows to obtain efficient 3D models, have been implemented and tested. The alignment problem has been investigated examining the various sources of errors during the image acquisition, in the radiological department, and during to the execution of the intervention. A rigid body registration procedure compatible with the surgical environment has been defined and implemented. The procedure has been integrated in a surgical navigation system and is useful as starting initial registration for more accurate alignment methods based on deformable approaches. Monoscopic and stereoscopic 3D localization machine vision routines, using the laparoscopic and/or generic cameras images, have been implemented to obtain intra-operative information that can be used to model abdominal deformations. Further, the use of this information for fusion and registration purposes allows to enhance the potentialities of computer assisted surgery. In particular a precise alignment between virtual and real anatomies for mixed-reality purposes, and the development of tracker-free navigation systems, has been obtained elaborating video images and providing an analytical adaptation of the virtual camera to the real camera. Clinical tests, demonstrating the usability of the proposed solutions, are reported. Test results and appreciation of radiologists and surgeons, to the proposed prototypes, encourage their integration in the daily clinical practice and future developments

강인한 대화형 영상 분할 알고리즘을 위한 시드 정보 확장 기법에 대한 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2021. 2. 이경무.Segmentation of an area corresponding to a desired object in an image is essential to computer vision problems. This is because most algorithms are performed in semantic units when interpreting or analyzing images. However, segmenting the desired object from a given image is an ambiguous issue. The target object varies depending on user and purpose. To solve this problem, an interactive segmentation technique has been proposed. In this approach, segmentation was performed in the desired direction according to interaction with the user. In this case, seed information provided by the user plays an important role. If the seed provided by a user contain abundant information, the accuracy of segmentation increases. However, providing rich seed information places much burden on the users. Therefore, the main goal of the present study was to obtain satisfactory segmentation results using simple seed information. We primarily focused on converting the provided sparse seed information to a rich state so that accurate segmentation results can be derived. To this end, a minimum user input was taken and enriched it through various seed enrichment techniques. A total of three interactive segmentation techniques was proposed based on: (1) Seed Expansion, (2) Seed Generation, (3) Seed Attention. Our seed enriching type comprised expansion of area around a seed, generation of new seed in a new position, and attention to semantic information. First, in seed expansion, we expanded the scope of the seed. We integrated reliable pixels around the initial seed into the seed set through an expansion step composed of two stages. Through the extended seed covering a wider area than the initial seed, the seed's scarcity and imbalance problems was resolved. Next, in seed generation, we created a seed at a new point, but not around the seed. We trained the system by imitating the user behavior through providing a new seed point in the erroneous region. By learning the user's intention, our model could e ciently create a new seed point. The generated seed helped segmentation and could be used as additional information for weakly supervised learning. Finally, through seed attention, we put semantic information in the seed. Unlike the previous models, we integrated both the segmentation process and seed enrichment process. We reinforced the seed information by adding semantic information to the seed instead of spatial expansion. The seed information was enriched through mutual attention with feature maps generated during the segmentation process. The proposed models show superiority compared to the existing techniques through various experiments. To note, even with sparse seed information, our proposed seed enrichment technique gave by far more accurate segmentation results than the other existing methods.영상에서 원하는 물체 영역을 잘라내는 것은 컴퓨터 비전 문제에서 필수적인 요소이다. 영상을 해석하거나 분석할 때, 대부분의 알고리즘들이 의미론적인 단위 기반으로 동작하기 때문이다. 그러나 영상에서 물체 영역을 분할하는 것은 모호한 문제이다. 사용자와 목적에 따라 원하는 물체 영역이 달라지기 때문이다. 이를 해결하기 위해 사용자와의 교류를 통해 원하는 방향으로 영상 분할을 진행하는 대화형 영상 분할 기법이 사용된다. 여기서 사용자가 제공하는 시드 정보가 중요한 역할을 한다. 사용자의 의도를 담고 있는 시드 정보가 정확할수록 영상 분할의 정확도도 증가하게 된다. 그러나 풍부한 시드 정보를 제공하는 것은 사용자에게 많은 부담을 주게 된다. 그러므로 간단한 시드 정보를 사용하여 만족할만한 분할 결과를 얻는 것이 주요 목적이 된다. 우리는 제공된 희소한 시드 정보를 변환하는 작업에 초점을 두었다. 만약 시드 정보가 풍부하게 변환된다면 정확한 영상 분할 결과를 얻을 수 있기 때문이다. 그러므로 본 학위 논문에서는 시드 정보를 풍부하게 하는 기법들을 제안한다. 최소한의 사용자 입력을 가정하고 이를 다양한 시드 확장 기법을 통해 변환한다. 우리는 시드 확대, 시드 생성, 시드 주의 집중에 기반한 총 세 가지의 대화형 영상 분할 기법을 제안한다. 각각 시드 주변으로의 영역 확대, 새로운 지점에 시드 생성, 의미론적 정보에 주목하는 형태의 시드 확장 기법을 사용한다. 먼저 시드 확대에 기반한 기법에서 우리는 시드의 영역 확장을 목표로 한다. 두 단계로 구성된 확대 과정을 통해 처음 시드 주변의 비슷한 픽셀들을 시드 영역으로 편입한다. 이렇게 확장된 시드를 사용함으로써 시드의 희소함과 불균형으로 인한 문제를 해결할 수 있다. 다음으로 시드 생성에 기반한 기법에서 우리는 시드 주변이 아닌 새로운 지점에 시드를 생성한다. 우리는 오차가 발생한 영역에 사용자가 새로운 시드를 제공하는 동작을 모방하여 시스템을 학습하였다. 사용자의 의도를 학습함으로써 효과적으로 시드를 생성할 수 있다. 생성된 시드는 영상 분할의 정확도를 높일 뿐만 아니라 약지도학습을 위한 데이터로써 활용될 수 있다. 마지막으로 시드 주의 집중을 활용한 기법에서 우리는 의미론적 정보를 시드에 담는다. 기존에 제안한 기법들과 달리 영상 분할 동작과 시드 확장 동작이 통합된 모델을 제안한다. 시드 정보는 영상 분할 네트워크의 특징맵과 상호 교류하며 그 정보가 풍부해진다. 제안한 모델들은 다양한 실험을 통해 기존 기법 대비 우수한 성능을 기록하였다. 특히 시드가 부족한 상황에서 시드 확장 기법들은 훌륭한 대화형 영상 분할 성능을 보였다.1 Introduction 1 1.1 Previous Works 2 1.2 Proposed Methods 4 2 Interactive Segmentation with Seed Expansion 9 2.1 Introduction 9 2.2 Proposed Method 12 2.2.1 Background 13 2.2.2 Pyramidal RWR 16 2.2.3 Seed Expansion 19 2.2.4 Re nement with Global Information 24 2.3 Experiments 27 2.3.1 Dataset 27 2.3.2 Implement Details 28 2.3.3 Performance 29 2.3.4 Contribution of Each Part 30 2.3.5 Seed Consistency 31 2.3.6 Running Time 33 2.4 Summary 34 3 Interactive Segmentation with Seed Generation 37 3.1 Introduction 37 3.2 Related Works 40 3.3 Proposed Method 41 3.3.1 System Overview 41 3.3.2 Markov Decision Process 42 3.3.3 Deep Q-Network 46 3.3.4 Model Architecture 47 3.4 Experiments 48 3.4.1 Implement Details 48 3.4.2 Performance 49 3.4.3 Ablation Study 53 3.4.4 Other Datasets 55 3.5 Summary 58 4 Interactive Segmentation with Seed Attention 61 4.1 Introduction 61 4.2 Related Works 64 4.3 Proposed Method 65 4.3.1 Interactive Segmentation Network 65 4.3.2 Bi-directional Seed Attention Module 67 4.4 Experiments 70 4.4.1 Datasets 70 4.4.2 Metrics 70 4.4.3 Implement Details 71 4.4.4 Performance 71 4.4.5 Ablation Study 76 4.4.6 Seed enrichment methods 79 4.5 Summary 82 5 Conclusions 87 5.1 Summary 89 Bibliography 90 국문초록 103Docto