A pragmatic approach for detecting liver cancer using image processing and data mining techniques

Anisha, P. R.; Reddy, Chandan K.; Prasad, L V Narasimha

doi:10.1109/spaces.2015.7058282

Cited by 30 publications

(9 citation statements)

References 15 publications

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“…In [14], computer aided diagnosis system is designed for liver cancer detection from the MRI, CT and US scan images. Segmentation of the liver image is done using K-means clustering technique.…”

Section: Results and Observationsmentioning

confidence: 99%

Liver and Tumor Segmentation Techniques for CT Abdominal Images

2019

IJITEE

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Image segmentation is one of the important step in digital image processing where the images are partitioned into different segments based on several properties like brightness, contrast, intensity and texture. Image processing includes several steps among which image segmentation is the difficult task. Accurate segmentation is the fundamental step in digital image processing. Segmentation can be performed manually, but as it is a tedious task, automatic segmentation techniques which gives more accuracy has to be found. Many recent segmentation techniques for liver image segmentation are discussed here. Some of the techniques to segment liver from CT scanned abdominal image and to segment tumor from the liver are discussed. The main objective is to highlight various techniques which can aid in developing a novel segmentation technique.

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Section: Results and Observationsmentioning

confidence: 99%

Liver and Tumor Segmentation Techniques for CT Abdominal Images

2019

IJITEE

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“…The work has achieved 84% Dice similarity for Neutrosophic set with thresholding method. In [5], K-means clustering method is used to segment the liver image. Haar wavelet transform is used for feature extraction which finds the threshold values of the regions of interest.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Liver from CT Abdominal Images

2019

IJITEE

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Automatic segmentation of liver from the abdominal Computed Tomography images is a difficult task. It is very important to segment the liver accurately, so the tumors can be located, detected and classified accurately within a liver. The proposed segmentation methods include preprocessing stage as first step where image resizing and grayscale conversion is performed. Thresholding technique is applied to obtain a binary image. Next, liver is segmented from 2-D abdominal CT scanned images using various segmentation methods like adaptive thresholding with morphological operations, global thresholding with morphological operations and Watershed gradient transform. Next, Active contour balloon snake model is applied on 3-D dataset 3D-IRCADb (3D Image Reconstruction for Comparison of Algorithm Database). The empirical comparative study is carried out using JSC, DSC, sensitivity, specificity and accuracy and results are tabulated. The empirical comparative study of these methods using Dice and Jaccard Similarity Coefficient is carried out and results are tabulated.

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“…Salah satu contohnya adalah untuk membantu dokter dalam menentukan tingkatan dari suatu kanker seperti kanker payudara [1], kanker mulut [2], dan kanker hati [3]. Selain itu, bidang lain seperti pertanian dan keamanan juga sudah dikembangkan dengan pengolahan citra.…”

Section: Pendahuluanunclassified

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Pujiharsono

2017

INFOTEL

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Abstrak -Perkembangan teknologi komputer membuat pengolahan citra saat ini banyak dikembangkan untuk dapat membantu manusia di berbagai bidang pekerjaan. Namun, tidak semua bidang pekerjaan dapat dikembangkan dengan pengolahan citra karena tidak mendukung penggunaan komputer sehingga mendorong pengembangan pengolahan citra dengan mikrokontroler atau mikroprosesor khusus. Perkembangan mikrokontroler dan mikroprosesor memungkinkan pengolahan citra saat ini dapat dikembangkan dengan embedded computer atau single board computer (SBC). Penelitian ini bertujuan untuk menguji kemampuan embedded computer dalam mengolah citra dan membandingkan hasilnya dengan komputer pada umumnya (general purpose computer). Pengujian dilakukan dengan mengukur waktu eksekusi dari empat operasi pengolahan citra yang diberikan pada sepuluh ukuran citra. Hasil yang diperoleh pada penelitian ini menunjukkan bahwa optimasi waktu eksekusi embedded computer lebih baik jika dibandingkan dengan general purpose computer dengan waktu eksekusi rata-rata embedded computer adalah 4-5 kali waktu eksekusi general purpose computer dan ukuran citra maksimal yang tidak membebani CPU terlalu besar untuk embedded computer adalah 256x256 piksel dan untuk general purpose computer adalah 400x300 piksel.Kata kunci -waktu eksekusi, bitmap, linux, flip, binerisasi, inversi, mean filter, single board computer (SBC) Abstract -The improvements in computer technology make image processing developed widely to help people in various areas of work. However, some work can't be developed with image processing because it doesn't support the use of computers and it encourages the development of image processing with a microcontroller or special microprocessor. The improvement of microcontrollers and microprocessors features currently allows image processing can be developed with embedded computers or single board computer (SBC). This study aims to test the performance of embedded computers for image processing and then compare the results with the performance of general purpose computer. The result shows that the optimized execution time of embedded computer is better than general purpose computer with the comparison of average execution time of embedded computer is 4-5 times slower than the general purpose computer and the maximal image size that does not make the CPU overload for embedded computer is 256x256 pixel and for general purpose computer is 400x300 pixels.

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A pragmatic approach for detecting liver cancer using image processing and data mining techniques

Cited by 30 publications

References 15 publications

Liver and Tumor Segmentation Techniques for CT Abdominal Images

Liver and Tumor Segmentation Techniques for CT Abdominal Images

Segmentation of Liver from CT Abdominal Images

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