• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송공학회 2009년도 IWAIT
• /
• pp.217-222
• /
• 2009

In this paper, a new approach is proposed for the segmentation of Computed Tomography (CT) head images. The approach consists of two-stage segmentation with each stage contains two different segmentation techniques. The ultimate aim is to segment the CT head images into three classes which are abnormalities, cerebrospinal fluid (CSF) and brain matter. For the first stage segmentation, k-means and fuzzy c-means (FCM) segmentation are implemented in order to acquire the abnormalities. Whereas for the second stage segmentation, modified FCM with population-diameter independent (PDI) and expectation-maximization (EM) segmentation are adopted to obtain the CSF and brain matter. The experimental results have demonstrated that the proposed system is feasible and achieve satisfactory results.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권3호
• /
• pp.962-975
• /
• 2020

The accurate segmentation of infant brain MR image into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is very important for early studying of brain growing patterns and morphological changes in neurodevelopmental disorders. Because of inherent myelination and maturation process, the WM and GM of babies (between 6 and 9 months of age) exhibit similar intensity levels in both T1-weighted (T1w) and T2-weighted (T2w) MR images in the isointense phase, which makes brain tissue segmentation very difficult. We propose a deep network architecture based on U-Net, called Triple Residual Multiscale Fully Convolutional Network (TRMFCN), whose structure exists three gates of input and inserts two blocks: residual multiscale block and concatenate block. We solved some difficulties and completed the segmentation task with the model. Our model outperforms the U-Net and some cutting-edge deep networks based on U-Net in evaluation of WM, GM and CSF. The data set we used for training and testing comes from iSeg-2017 challenge (http://iseg2017.web.unc.edu).

• International Journal of Computer Science & Network Security
• /
• 제22권5호
• /
• pp.143-148
• /
• 2022

Brain tumor classification is an important process that allows doctors to plan treatment for patients based on the stages of the tumor. To improve classification performance, various CNN-based architectures are used for brain tumor classification. Existing methods for brain tumor segmentation suffer from overfitting and poor efficiency when dealing with large datasets. The enhanced CNN architecture proposed in this study is based on U-Net for brain tumor segmentation, RefineNet for pattern analysis, and SegNet architecture for brain tumor classification. The brain tumor benchmark dataset was used to evaluate the enhanced CNN model's efficiency. Based on the local and context information of the MRI image, the U-Net provides good segmentation. SegNet selects the most important features for classification while also reducing the trainable parameters. In the classification of brain tumors, the enhanced CNN method outperforms the existing methods. The enhanced CNN model has an accuracy of 96.85 percent, while the existing CNN with transfer learning has an accuracy of 94.82 percent.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2019년도 춘계학술대회
• /
• pp.432-435
• /
• 2019

Convolutional Neural Networks (CNNs) have recently been gaining popularity in the medical image analysis field because of their image segmentation capabilities. In this paper, we present a CNN that performs automated brain tumor segmentations of sparsely annotated 3D Magnetic Resonance Imaging (MRI) scans. Our CNN is based on 3D U-net architecture, and it includes separate Dilated and Depth-wise Convolutions. It is fully-trained on the BraTS 2018 data set, and it produces more accurate results even when compared to the winners of the BraTS 2017 competition despite having a significantly smaller amount of parameters.

• 대한전자공학회논문지TC
• /
• 제46권12호
• /
• pp.102-107
• /
• 2009

기술의 발달로 의료장비의 현대화가 이루어지고 PACS와 같은 시스템이 보편화되면서 디지털 의료영상처리 기술에 대한 관심이 높아지고 있다. 영역분할 기술은 디지털의료영상처리에서 첫 번째 단계로 필요한 전처리기술이다. 영역분할을 통하여 특정 부위가 종양, 부종, 파손 및 괴사세포와 같은 이상 현상을 나타내는 것을 조기에 발견할 수 있도록 해주고, 의사들이 적절한 처방을 내려줄 수 있도록 도와줄 수 있다. 특히 두뇌영상에서 백질, 회백질 및 CSF(cerebral spinal fluid)의 영역분할은 두뇌연구의 핵심기술이다. 이들 의료영상에서 기존의 윤곽선이나 영역 확장법은 애매한 경계선과 장기내의 물리적 특성이 비균질하여 영역분할의 실패율을 높게 한다. 퍼지기반의 영역분할 알고리듬은 불분명한 경계를 이루는 장기의 영역분할에 강하다고 알려져 있다. 본 연구에서는 자기공명영상이 강하게 나타내는 잡음에도 안정적인 퍼지기반의 영역분할 알고리듬을 제안하였다. 제안된 알고리듬은 이웃화소들을 군집시킬 때에 평균과 분산의 정보를 이용하여 최소한의 계산을 추가함으로써, 기존의 퍼지기반 영역분할 방법에 비하여 실패율이 대략 30% 이하로 낮은 것을 확인하였다.

• 정보처리학회논문지A
• /
• 제13A권5호
• /
• pp.465-472
• /
• 2006

최근 대용량 의료영상 데이터로부터 인체 기관 또는 질환 부위 추출을 위한 영상 분할 기법이 매우 다양하게 제안되고 있으나, 뇌와 같이 다중 구조를 가지면서 구조간 경계 구분이 어려운 영상의 구조적 분할에는 한계를 가진다. 이를 위해 주로 복셀을 유한 개의 군집으로 분류하는 군집화 (clustering) 기법이 이용되나 이는 개별 복셀 단위의 연산을 수행함으로 인해 잡음의 영향을 받는 제한점이 있다. 그러므로 잡음의 영상을 최소화하고 영상 경계를 강화시키는 향상기법을 적용함으로써 보다 견고한 구조적 분할을 수행할 수 있다. 본 연구에스는 뇌 자기공명영상에 대하여 백질(white matter), 회백질(gray matter), 뇌척수액(cerebrospinal fluid)의 내부 구조를 효율적으로 추출하기 위한 필터링 기반 군집화에 의한 구조적 분할 기법을 제안한다. 우선 구조간 경계를 강화하고 구조 내 잡음을 약화시키기 위해 응집성 향상 확산 필터링(coherence enhancing diffusiion filtering)을 적용한다. 또한 이 과정을 통해 강화된 영상에 퍼지 c-means 군집화 기법을 적용하여 각 복셀이 속하는 구조에 해당하는 군집의 인덱스를 할당함으로써 구조적 분할을 수행한다. 제안된 구조적 분할기법은 기존의 가우시안 또는 일반적인 비등방성 확산 필터링과 군집화 기법을 적용한 기법에 비해 전문가의 수동분할 결과와의 일치 비율에 의한 분할 정확도를 향상시킴을 보였다. 또한 경계 부분에 있어서의 세밀한 분할을 통해 재생산 가긍하고 사용자 수동후 처리를 최소화할 수 있는 결과를 제시함으로써 형태적 뇌 이상 진단을 위한 효율적인 보조 수단을 제공한다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권10호
• /
• pp.2788-2808
• /
• 2023

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

• 한국멀티미디어학회논문지
• /
• 제17권12호
• /
• pp.1412-1417
• /
• 2014

The corpus callosum is the largest part of the brain, which is related to many neurological diseases. Snake model or active contour model is widely used in medical image processing field, especially image segmentation they look into the nearby edge, localizing them accurately. In this paper, corpus callosum segmentation using the snake model, is proposed. We tested a snake model on brain MRI. Then we compared the result with an active shape approach and found that snake model had better segmentation accuracy also faster than active shape approach.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권3호
• /
• pp.1167-1187
• /
• 2020

With the development of medical imaging technology, image registration has been widely used in the field of disease diagnosis. The registration between different modal images of brain magnetic resonance (MR) is particularly important for the diagnosis of brain diseases. However, previous registration methods don't take advantage of the prior knowledge of bilateral brain symmetry. Moreover, the difference in gray scale information of different modal images increases the difficulty of registration. In this paper, a multimodal medical image registration method based on image segmentation and symmetric self-similarity is proposed. This method uses modal independent self-similar information and modal consistency information to register images. More particularly, we propose two novel symmetric self-similarity constraint operators to constrain the segmented medical images and convert each modal medical image into a unified modal for multimodal image registration. The experimental results show that the proposed method can effectively reduce the error rate of brain MR multimodal medical image registration with rotation and translation transformations (average 0.43mm and 0.60mm) respectively, whose accuracy is better compared to state-of-the-art image registration methods.

• 한국멀티미디어학회논문지
• /
• 제17권5호
• /
• pp.566-572
• /
• 2014

The hippocampus has been known as one of the most important structure related to many neurological disorders, such as Alzheimer's disease. This paper presents the snake model to segment hippocampus from brain MRI. The snake model or active contour model is widely used in medical image processing fields, especially image segmentation they look onto nearby edge, localizing them accurately. We applied a snake model on brain MRI. Then we compared our results with an active shape approach. The results show that hippocampus was successfully segmented by the snake model.