{"title":"脑肿瘤定量体积测量在治疗和计划中的应用","authors":"K. V. Ahammed Muneer, S. Pranav","doi":"10.1109/ICACC48162.2019.8986174","DOIUrl":null,"url":null,"abstract":"The disease progression of malignant tumors can be effectively indicated from their quantitative analysis. This article presents a faster and an accurate quantification of brain tumor volume from glioma MRI images. The tumor volume is a prime presaging factor for treatment of the disease and also to fix the treatment plans according to the severity of the disease. In our experimentation, we used T1-weighted glioma MRI images obtained from the hospital clinics. The first phase of the work comprising of the segmentation of tumor portion using semi-automatic methods like seeded region growing and morphological operations based algorithms. Secondly, the area of tumor portion of all tumor affected slices is calculated and this value is multiplied with the slice thickness to obtain the gross tumor volume. It should be noted that the slice thickness and pixel resolution depends on the MRI machine under test. The speed and accuracy for the volume measurement is analysed using the two mentioned segmentation algorithms. Results show that the morphological operations based method yields good segmented region compared to the region growing method. It is also observed that morphological operations based method is faster in segmenting the effective tumor region and hence to calculate the volume.","PeriodicalId":305754,"journal":{"name":"2019 9th International Conference on Advances in Computing and Communication (ICACC)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Quantitative Volume Measurement of Brain Tumor for Treatment and Planning\",\"authors\":\"K. V. Ahammed Muneer, S. Pranav\",\"doi\":\"10.1109/ICACC48162.2019.8986174\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The disease progression of malignant tumors can be effectively indicated from their quantitative analysis. This article presents a faster and an accurate quantification of brain tumor volume from glioma MRI images. The tumor volume is a prime presaging factor for treatment of the disease and also to fix the treatment plans according to the severity of the disease. In our experimentation, we used T1-weighted glioma MRI images obtained from the hospital clinics. The first phase of the work comprising of the segmentation of tumor portion using semi-automatic methods like seeded region growing and morphological operations based algorithms. Secondly, the area of tumor portion of all tumor affected slices is calculated and this value is multiplied with the slice thickness to obtain the gross tumor volume. It should be noted that the slice thickness and pixel resolution depends on the MRI machine under test. The speed and accuracy for the volume measurement is analysed using the two mentioned segmentation algorithms. Results show that the morphological operations based method yields good segmented region compared to the region growing method. It is also observed that morphological operations based method is faster in segmenting the effective tumor region and hence to calculate the volume.\",\"PeriodicalId\":305754,\"journal\":{\"name\":\"2019 9th International Conference on Advances in Computing and Communication (ICACC)\",\"volume\":\"80 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 9th International Conference on Advances in Computing and Communication (ICACC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACC48162.2019.8986174\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 9th International Conference on Advances in Computing and Communication (ICACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACC48162.2019.8986174","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quantitative Volume Measurement of Brain Tumor for Treatment and Planning
The disease progression of malignant tumors can be effectively indicated from their quantitative analysis. This article presents a faster and an accurate quantification of brain tumor volume from glioma MRI images. The tumor volume is a prime presaging factor for treatment of the disease and also to fix the treatment plans according to the severity of the disease. In our experimentation, we used T1-weighted glioma MRI images obtained from the hospital clinics. The first phase of the work comprising of the segmentation of tumor portion using semi-automatic methods like seeded region growing and morphological operations based algorithms. Secondly, the area of tumor portion of all tumor affected slices is calculated and this value is multiplied with the slice thickness to obtain the gross tumor volume. It should be noted that the slice thickness and pixel resolution depends on the MRI machine under test. The speed and accuracy for the volume measurement is analysed using the two mentioned segmentation algorithms. Results show that the morphological operations based method yields good segmented region compared to the region growing method. It is also observed that morphological operations based method is faster in segmenting the effective tumor region and hence to calculate the volume.