Computer-aided Classification of Liver Lesions from CT Images Based on Multiple ROI

Alahmer, Hussein; Ahmed, Amr

doi:10.1016/j.procs.2016.07.027

Cited by 46 publications

(17 citation statements)

References 17 publications

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“…It is the third cause of cancer death and results in the death of around 700,000 people each year worldwide [4]. The major risk factors for primary liver cancers are cirrhosis resulted from alcohol usage, hepatitis B and C viruses, and a fatty liver disease caused by obesity [5]. It can be diagnosed and detected by using different imaging tests like ultrasound, magnetic resonance imaging (MRI) and computed tomography (CT).…”

Section: Introductionmentioning

confidence: 99%

“…And this abdominal CT image is further processed in order to segment the liver tumor from the image. But still, the intensity similarity between tumor and other nearby tissue in the CT images makes the detection of tumor too difficult [5]. Therefore, these images need to be processed and enhanced in order to early detect and differentiate cancerous tissue.…”

Section: Introductionmentioning

confidence: 99%

“…The manual segmentation of CT scan images is laborious and time-consuming for a clinical setting. Due to various factors, such as the liver typically stretching over 150 slices in a CT volume, indefinite shape of the lesions and low-intensity contrast between lesions and nearby tissues, the variation of liver shape and size among patients and the similarity with other organs of almost the same intensity [5], [8]. Considering those problems, researchers have designed different computer-aided diagnostic systems for the segmentation of liver and tumor from the abdominal CT scan images.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Liver Cancer Segmentation from Computed Tomography Images

Ayalew

Fante

Aliy

2020

Preprint

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Background: Liver cancer is the sixth most common cancer worldwide. According to WHO data in 2017, the liver cancer death in Ethiopia reached 1040 (0.16%) from all cancer deaths. Hepatocellular carcinoma (HCC), primary liver cancer causes the death of around 700,000 people each year worldwide and this makes it the third leading cause of cancer death. HCC is occurred due to cirrhosis and hepatitis B or C viruses. Liver cancer mostly diagnosed with a computed tomography (CT) scan. But, the detection of the tumor from the CT scan image is difficult since tumors have similar intensity with nearby tissues and may have a different appearance depending on their type, state, and equipment setting. Nowadays deep learning methods have been used for the segmentation of liver and its tumor from the CT scan images and they are more efficient than those traditional methods. But, they are computationally expensive and need many labeled samples for training, which are difficult in the case of biomedical images. Results: A deep learning-based segmentation algorithm is employed for liver and tumor segmentation from abdominal CT scan images. Three separate UNet models, one for liver segmentation and the others two for tumor segmentation from the segmented liver and directly from the abdominal CT scan image were used. A dice score of 0.96 was obtained for liver segmentation. And a dice score of 0.74 and 0.63 was obtained for segmentation of tumor from the liver and from abdominal CT scan image respectively. Conclusion: The research improves the liver tumor segmentation that will help the physicians in the diagnosis and detection of liver tumors and in designing a treatment plan for the patient. And for the patient, it increases the patients’ chance of getting treatment and decrease the mortality rate due to liver cancer.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Liver Cancer Segmentation from Computed Tomography Images

Ayalew

Fante

Aliy

2020

Preprint

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“…H. Alahmer and A. Ahmed [10] proposed system to extracting the main features took from multiple ROIs in order to diagnose the type of liver abrasion to Benign or Malignant. The researchers divided the lesion region into three parts inside part, outside part, and the border part.…”

Section: Introductionmentioning

confidence: 99%

Diagnosing of some hepatic lesions from light microscope images based on morphological and texture features

Jabr

Hasan

2020

IJEECS

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One of the common problems observed in medicines is hepatotoxicity as liver play mainly role in metabolizes the herbal medicines. Although, the acceptance of herbal medicines is growing nowadays still there is an absence of knowledge about their toxicological properties and the right use being a hepatotoxic.This paper presents method to detect and diagnoses liver lesions in four types: necrotic cells, fatty degenerative cells, hepatocellular hypertrophic cells and congested cells using image processing techniques. The method is proposed to perform two tasks the first is conclude whether the liver image is normal or abnormal the second if abnormal state is detected then diagnosis lesions type must performs. The method progresses in many steps are preprocessing, features extraction, classification and lesion diagnosing. Grey level co-occurrence Matrix (GLCM) technique is utilize to concentrate features to distinguish between normal and abnormal case using neural network classifier if abnormal state is detected the method feedback with colour image to analyse cells shape and image intensity colour to determine which type of diseases founded in image based on statistical and morphological features of cells. The method tested on 107 images it is got on the accuracy 100% in classification and 95% in diagnosing.

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“…The classification accuracy turned out to be 95%. Alahmer and Ahmed [17] segmented the liver tumor for classification based upon the region and edge-based texture analysis. Multiple regions of interests (ROI) were chosen to classify the malignant and benign liver tumors using SVM which yield a classification accuracy of 98%.…”

Section: Introductionmentioning

confidence: 99%

Classification of Abdominal CT Images bearing Liver Tumor Using Structural Similarity Index and Support Vector Machine

Siddiqi

Khawaja

Hashmi³

2020

Mehran Univ. res. j. eng. technol.

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Computed Tomographic (CT) imaging is extensively implemented for liver tumor visualization and detection. Computer aided image processing algorithms can provide aid to the physicians and radiologists in detecting deadly diseases of liver specifically cancerous liver tumors. This paper presents a novel image processing technique to automatically classify liver for its abnormality without going through the liver segmentation stage. The study is conducted on a dataset of 39 samples of abdominal CT images. The CT dataset comprises of images bearing unhealthy liver. The unhealthy liver is further divided into livers bearing malignant tumors namely hepatoma and benign tumors namely hemangiomas. The methodology adopted for the study comprises of feature extraction from original CT images of all types of liver with special focus on textural information. The extracted features undergo the process of classification for malignancy and benignancy of the liver tumor. The classifiers used for textural feature analysis include Support Vector Machines (SVM), K-Nearest Neighbor (KNN) and ensemble classifier. Amongst these classifiers SVM yields a classification accuracy of 100% as compare to KNN and ensemble classifier which give the classification accuracy of 94.7% and 52.6% respectively. The proposed method of classification applied on entire abdominal CT scans without segmentation is performed by using the feature extraction matrix of structural similarity index (SSIM), which gives an improved classification accuracy of 100% as compared to the traditional GLCM matrix. The methodology can be tested to classify the liver for malignancy using other non-invasive techniques of ultrasounds and Magnetic Resonance Imaging (MRI) as well.

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Computer-aided Classification of Liver Lesions from CT Images Based on Multiple ROI

Cited by 46 publications

References 17 publications

Deep Learning Based Liver Cancer Segmentation from Computed Tomography Images

Deep Learning Based Liver Cancer Segmentation from Computed Tomography Images

Diagnosing of some hepatic lesions from light microscope images based on morphological and texture features

Classification of Abdominal CT Images bearing Liver Tumor Using Structural Similarity Index and Support Vector Machine

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