Study on the Method of Constructing a Statistical Shape Model and Its Application to the Segmentation of Internal Organs in Medical Images

In image processing, segmentation is one of the critical tasks for diagnostic analysis and image interpretation. In the following thesis, we describe the investigation of three problems related to the segmentation algorithms for medical images: Active shape model algorithm, 3-dimensional (3-D) statistical shape model building and organic segmentation experiments. For the development of Active shape models, the constraints of statistical model reduced this algorithm to be difficult for various biological shapes. To overcome the coupling of parameters in the original algorithm, in this thesis, the genetic algorithm is introduced to relax the shape limitation. How to construct a robust and effective 3-D point model is still a key step in statistical shape models. Generally the shape information is obtained from manually segmented voxel data. In this thesis, a two-step procedure for generating these models was designed. After transformed the voxel data to triangular polygonal data, in the first step, attitudes of these interesting objects are aligned according their surface features. We propose to reflect the surface orientations by means of their Gauss maps. As well the Gauss maps are mapped to a complex plane using stereographic projection approach. The experiment was run to align a set of left lung models. The second step is identifying the positions of landmarks on polygonal surfaces. This is solved by surface parameterization method. We proposed two simplex methods to correspond the landmarks. A semi-automatic method attempts to “copy” the phasic positions of pre-placed landmarks to all the surfaces, which have been mapped to the same parameterization domain. Another automatic corresponding method attempts to place the landmarks equidistantly. Finally, the goodness experiments were performed to measure the difference to manually corresponded results. And we also compared the affection to correspondence when using different surface mapping methods. The third part of this thesis is applying the segmentation algorithms to solve clinical problems. We did not stick to the model-based methods but choose the suitable one or their complex according to the objects. In the experiment of lung regions segmentation which includes pulmonary nodules, we propose a complementary region growing method to deal with the unpredictable variation of image densities of lesion regions. In the experiments of liver regions, instead of using region growing method in 3-D style, we turn into a slice-by-slice style in order to reduce the overflows. The image intensity of cardiac regions is distinguishable from lung regions in CT image. But as to the adjacent zone of heart and liver boundary are generally blurry. We utilized a shape model guided method to refine the segmentation results.3-D segmentation techniques have been applied widely not only in medical imaging fields, but also in machine vision, computer graphic. At the last part of this thesis, we resume some interesting topics such as 3-D visualization for medical interpretation, human face recognition and object grasping robot etc.九州工業大学博士学位論文 学位記番号:工博甲第353号　学位授与年月日:平成25年9月27日Chapter 1: Introduction|Chapter 2: Framework of Medical Image Segmentation|Chapter 3: 2-D Organic Regions Using Active Shape Model and Genetic Algorithm|Chapter 4: Alignment of 3-D Models|Chapter 5: Corespondence of 3-D Models|Chapter 6:Experiments of Organic Segmentation|Chapter 7: Visualization Technology and Its Applications|Chapter 8: Conclusions and Future Works九州工業大学平成25年

Hybrid Beamforming via the Kronecker Decomposition for the Millimeter-Wave Massive MIMO Systems

Despite its promising performance gain, the realization of mmWave massive MIMO still faces several practical challenges. In particular, implementing massive MIMO in the digital domain requires hundreds of RF chains matching the number of antennas. Furthermore, designing these components to operate at the mmWave frequencies is challenging and costly. These motivated the recent development of hybrid-beamforming where MIMO processing is divided for separate implementation in the analog and digital domains, called the analog and digital beamforming, respectively. Analog beamforming using a phase array introduces uni-modulus constraints on the beamforming coefficients, rendering the conventional MIMO techniques unsuitable and call for new designs. In this paper, we present a systematic design framework for hybrid beamforming for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factors being uni-modulus vectors. Exploiting properties of Kronecker mixed products, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Furthermore, a channel estimation scheme is designed for enabling the proposed hybrid beamforming. The scheme estimates the AoA of data and interference paths by analog beam scanning and data-path gains by analog beam steering. The performance of the channel estimation scheme is analyzed. In particular, the AoA spectrum resulting from beam scanning, which displays the magnitude distribution of paths over the AoA range, is derived in closed-form. It is shown that the inter-cell interference level diminishes inversely with the array size, the square root of pilot sequence length and the spatial separation between paths.Comment: Submitted to IEEE JSAC Special Issue on Millimeter Wave Communications for Future Mobile Networks, minor revisio

6G Non-Terrestrial Networks Enabled Low-Altitude Economy: Opportunities and Challenges

The unprecedented development of non-terrestrial networks (NTN) utilizes the low-altitude airspace for commercial and social flying activities. The integration of NTN and terres- trial networks leads to the emergence of low-altitude economy (LAE). A series of LAE application scenarios are enabled by the sensing, communication, and transportation functionalities of the aircrafts. The prerequisite technologies supporting LAE are introduced in this paper, including the network coverage and aircrafts detection. The LAE functionalities assisted by aircrafts with respect to sensing and communication are then summarized, including the terrestrial and non-terrestrial targets sensing, ubiquitous coverage, relaying, and traffic offloading. Finally, several future directions are identified, including aircrafts collaboration, energy efficiency, and artificial intelligence enabled LAE.Comment: This paper has been submitted to IEEE for possible publicatio

Dynamic fuzzy multiple criteria decision making for performance evaluation

The paper proposes a dynamic fuzzy multiple criteria decision making (DFMCDM) method. The method considers the integrated weight of the decision makers with the subjective and objective preference and the effect of time weight. In the proposed method, a mathematical programming model is used to determine the integrated weight, and a basic unit-interval monotonic (BUM) function based approach is used to calculate the time weight. In addition, a distance measure of membership function is introduced to effectively measure the degree of difference between the alternatives in the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). Finally, a numerical example is introduced to illustrate the proposed method

Quantitative analysis of choroidal alterations in thyroid eye disease using swept-source OCT

Purpose: To investigate choroidal alterations in patients with thyroid eye disease (TED) using swept-source optical coherence tomography (SS-OCT) and compare them with age-matched healthy controls.Methods: SS-OCT scans were performed to obtain quantitative measurements of choroidal parameters. Mean choroidal thickness (MCT), choroidal vessel volume (CVV), choroidal stroma volume (CSV), choroidal vascularity index (CVI), and choroidal stroma-to-vessel volume ratio (CSVR) were calculated and compared between TED and control eyes.Results: TED eyes exhibited significantly higher MCT (276.25 ± 58.75 μm vs. 236.86 ± 45.02 μm, p < 0.001), CVV (21.46 ± 5.10 mm3vs. 18.14 ± 3.83 mm3, p = 0.001), and CSV (13.86 ± 2.80 mm3vs. 11.44 ± 2.17 mm3, p < 0.001) compared to control eyes. However, there were no significant differences in CVI (0.61 ± 0.02 vs. 0.61 ± 0.03, p = 0.838) or CSVR (0.65 ± 0.05 vs. 0.64 ± 0.07, p = 0.345) between the two groups.Conclusion: SS-OCT effectively differentiated TED eyes from normal eyes based on choroidal alterations. The increased MCT, CVV, and CSV in TED suggest both dilated choroidal vasculature and expanded choroidal stroma. These findings highlight the potential of SS-OCT as an adjunctive imaging tool for the assessment of TED

Integrating Sensing, Communication, and Power Transfer: Multiuser Beamforming Design

In the sixth-generation (6G) networks, massive low-power devices are expected to sense environment and deliver tremendous data. To enhance the radio resource efficiency, the integrated sensing and communication (ISAC) technique exploits the sensing and communication functionalities of signals, while the simultaneous wireless information and power transfer (SWIPT) techniques utilizes the same signals as the carriers for both information and power delivery. The further combination of ISAC and SWIPT leads to the advanced technology namely integrated sensing, communication, and power transfer (ISCPT). In this paper, a multi-user multiple-input multiple-output (MIMO) ISCPT system is considered, where a base station equipped with multiple antennas transmits messages to multiple information receivers (IRs), transfers power to multiple energy receivers (ERs), and senses a target simultaneously. The sensing target can be regarded as a point or an extended surface. When the locations of IRs and ERs are separated, the MIMO beamforming designs are optimized to improve the sensing performance while meeting the communication and power transfer requirements. The resultant non-convex optimization problems are solved based on a series of techniques including Schur complement transformation and rank reduction. Moreover, when the IRs and ERs are co-located, the power splitting factors are jointly optimized together with the beamformers to balance the performance of communication and power transfer. To better understand the performance of ISCPT, the target positioning problem is further investigated. Simulations are conducted to verify the effectiveness of our proposed designs, which also reveal a performance tradeoff among sensing, communication, and power transfer.Comment: This paper has been submitted to IEEE for possible publicatio

Semantic Characteristics Prediction of Pulmonary Nodule Using Artificial Neural Networks

Since it is difficult to choose which computer calculated features are effective to predict the malignancy of pulmonary nodules, in this study, we add a semantic-level of Artificial Neural Networks (ANNs) structure to improve intuition of features selection. The works of this study include two: 1) seeking the relationships between computer-calculated features and medical semantic concepts which could be understood by human; 2) providing an objective assessment method to predict the malignancy from semantic characteristics. We used 60 thoracic CT scans collected from the Lung Image Database Consortium (LIDC) database, in which the suspicious lesions had been delineated and annotated by 4 radiologists independently. Corresponding to the two works of this study, correlation analysis experiment and agreement experiment were performed separately.The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC\u2713), July 3-7, 2013, Osaka, Japa

Semantic Characteristics Prediction of Pulmonary Nodule Using Artificial Neural Networks

The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'13), July 3-7, 2013, Osaka, JapanSince it is difficult to choose which computer calculated features are effective to predict the malignancy of pulmonary nodules, in this study, we add a semantic-level of Artificial Neural Networks (ANNs) structure to improve intuition of features selection. The works of this study include two: 1) seeking the relationships between computer-calculated features and medical semantic concepts which could be understood by human; 2) providing an objective assessment method to predict the malignancy from semantic characteristics. We used 60 thoracic CT scans collected from the Lung Image Database Consortium (LIDC) database, in which the suspicious lesions had been delineated and annotated by 4 radiologists independently. Corresponding to the two works of this study, correlation analysis experiment and agreement experiment were performed separately