Penggabungan Fitur Bentuk dan Fitur Tekstur yang Invariant terhadap Rotasi untuk Klasifikasi Citra Pap Smear
DOI:
https://doi.org/10.24002/jbi.v7i1.479Abstract
Abstract. Pap test is a cervical cancer screening manually and requires a long time that it needs an exact cell classification system based computers. Features determination by observation in characteristic differences between the datasets visually betweenclass will help a cell classification results which has relevant characteristics between classes. In addition, the change in orientation of the cells at the time of the acquisition will affect the value of the generated feature so extraction method that is rotation invariant is needed to overcome that problem. This research proposes the combination of simple shapes feature and the texture feature from extraction Local Binary Pattern Histogram Fourier (LBP-HF) that invariant to rotation as additional features to classify pap smear images. The result show that the proposed feature combination yield good performance with accuracy 92.44% for two category cell and 70.06% for seven class cell.
Keywords: classification, lbp-hf, Â pap smear image, shape feature.
Abstrak. Pap test adalah pemeriksaan kanker serviks secara manual yang membutuhkan waktu yang lama sehingga dibutuhkan sistem klasifikasi sel berbasis komputer yang tepat. Penentuan fitur melalui observasi pada perbedaan ciri antarkelas secara visual pada dataset akan membantu hasil klasifikasi sel untuk mendapatkan ciri yang relevan antarkelas. Selain itu, adanya perubahan orientasi sel pada saat akuisisi akan mempengaruhi nilai fitur yang dihasilkan sehingga dibutuhkan metode ekstraksi fitur yang invariant terhadap rotasi. Penelitian ini mengusulkan penggabungan fitur bentuk sederhana dan fitur tekstur dengan ekstraksi fitur Local Binary Pattern –Histogram Fourier yang invariant terhadap rotasi sebagai ciri tambahan dalam mengklasifikasikan citra pap smear. Hasilnya menunjukkan bahwa kombinasi fitur menghasilkan performa yang baik dengan akurai 92,44% untuk dua kategori sel dan 70,06% untuk tujuh kelas sel.
Kata Kunci: klasifikasi, lbp-hf, citra pap smear, fitur bentuk.
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