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Journal : Jurnal Informatika

EVALUASI CIRI CITRA MEDIS MENGGUNAKAN METODE PENINGKATAN KUALITAS CITRA HISTOGRAM EQUALIZATION DAN KARAKTERISASI STATISTIK Afriliana Kusumadewi; Sugeng Santoso
Jurnal Informatika Vol 11, No 1 (2015): Jurnal Teknologi Komputer dan Informatika
Publisher : Universitas Kristen Duta Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (11707.181 KB) | DOI: 10.21460/inf.2015.111.414

Abstract

This research has an objective to implement histogram equalization technique in the evaluation of characteristics medical images, especially for statistical characterization of medical images. Implementation starting with the pre-processing of the image by converting RGB medical images into grayscale images, followed by determination of the Region of Interest (ROI) and cropping methods. The next process was to increase the quality of medical images by implementing histogram equalization technique. The medical image characteristic was evaluated using characteristic statistical methods with their parameters are mean value, standard deviation, entropy, skewness, and kurtosis. The Medical image was used in this research is a medical image of advanced breast cancer. This research compared the characteristics statistical characterization of medical images obtained with the original medical image which has enhanced use histogram equalization technique. The results of implementation histogram equalization obtain medical image feature extraction results for the value of mean, entropy, skewness, and kurtosis become smaller and standard deviation value was increase.
PENERAPAN ALGORITMA K-MEANS PADA KOMPRESI ADAPTIF CITRA MEDIS MRI I Wayan Angga Wijaya; Afriliana Kusumadewi
Jurnal Informatika Vol 11, No 2 (2015): Jurnal Teknologi Komputer dan Informatika
Publisher : Universitas Kristen Duta Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (323.404 KB) | DOI: 10.21460/inf.2015.112.450

Abstract

MRI medical image processing require large amounts of memory. Due to limited bandwidth and storage capacity, the image must be compressed prior to transmission and stored. This paper has the objective to implement the algorithm k means the MRI medical image compression. Implementation begins with the Pre post. At this stage, L-dimensional vector of the image will be made. L is the block - a measure used for clustering technique, but is set back in the form of an array. Then the process of clustering. At this stage, every pixel of the image is represented by the centroid of the cluster. And the last stage is the Main Compression, the pixels that do not contain important information will be removed. The study compared the quality of the original image and compressed image. Based on manual observation, there is no significant difference in quality between the original image and the compressed one.