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"Alat Ultrasonografi (USG) merupakan alat yang paling sering digunakan untuk melakukan pemeriksaan janin dalam kandungan. Hal ini dikarenakan selain mampu memberikan gambaran terhadap keadaan janin dengan baik, alat ini bebas dari radiasi ionisasi sehingga tergolong aman. Penelitian ini bertujuan untuk mengembangkan metode yang mampu melakukan proses deteksi dan pengukuran biometri janin secara otomatis khususnya biometri kepala janin. Adapun biometri tersebut adalah head circumference (HC) dan biparietal diameter (BPD) yang merupakan salah satu parameter yang sering digunakan oleh dokter untuk mengetahui umur serta pertumbuhan janin dalam kandungan. Kedua biometri ini dapat diukur dengan melakukan aproksimasi terhadap bentuk elips. Untuk melakukan proses ini maka diperlukan tahapan dimulai dengan melakukan segmentasi citra dengan teknik thresholding. Selanjutnya dilakukan proses deteksi menggunakan metode Particle Swarm Optimization (PSO) dengan memanfaatkan fitness function yang diperoleh dari hasil vote menggunakan metode Randomized Hough Transform (RHT). Hasil pengukuran oleh sistem dibandingkan dengan hasil pengukuran secara manual oleh pakar. Uji coba juga dilakukan terhadap data sintetis dengan density noise 0,1 hingga 0,7. Dari hasil eksperimen diperoleh bahwa metode yang dikembangkan lebih baik daripada metode RHT, IRHT dan mEPSOHT untuk melakukan deteksi elips pada citra tersebut. Hasil eksperimen terhadap data sebenarnya yaitu USG 2D kepala janin diperoleh hasil rata-rata nilai hit dari metode yang dikembangkan lebih tinggi daripada metode lainnya namun hasil interrun dan interobserver variation tidak lebih baik dari metode lainnya. Hal ini dikarenakan metode yang dikembangkan lebih cenderung untuk terjebak pada local best dan tidak selalu tepat untuk melakukan deteksi pada citra kepala janin.

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The application in ultrasonography (USG) is a tool that most often used to examine fetus in the womb. At this study will perform image processing on biomedical images especially for fetus in the womb using two dimensional ultrasound device (USG 2D).

The aim of this study is to develop a system that is capable to perform detection and measurement of fetal biometry automatically. The biometric used in this research consists of head circumference (HC) and biparietal diameter (BPD) analysis. BPD and HC are parameters which are often used by doctors to determine the state of the fetus in the womb. Both biometric parameters can be measured by performing an approximation of the elliptical shape. To do this process, it is necessary to start from segmentation images by thresholding techniques. After preprocessing is completed then the next stage of the detection process is carried out by using Particle Swarm Optimization (PSO). PSO fitness function is obtained from voting in Randomized Hough Transform (RHT) method.

The measurement results by proposed method are then compared with the results obtained manually by experts. A trial has also been conducted on the synthetic data with noise density 0.1 to 0.7. Experiment results show that the proposed method is better than the other methods e.g. RHT, mEPSOHT and IRHT in detecting ellipse. Further trials have been conducted on actual data i.e. 2D ultrasound fetal head data.

From the experiement we have found that the average hit value of our proposed method is higher than other methods. However, the results of interrun and interobserver variation are not better than others. This is because our developed method is more likely to be trapped in local best and doest not always correctly detect ellipse of the fetal head images."

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