SEGMENTASI PARASIT MALARIA MENGGUNAKAN OPERASI MORFOLOGI CITRA

Abstrak. Penyakit malaria masih menjadi masalah kesehatan di Indonesia. Masih banyak korban jiwa akibat penyakit malaria, terutama di Maluku dan Papua. Penelitian ini bertujuan membuat sistem segmentasi otomatis parasit malaria. Tahapan proses yang dilakukan adalah melakukan konversi ke ruang warna HSV dengan mengambil komponen S (Saturation) kemudian dilanjutkan dengan Operasi Morfologi . Hasil yang didapat dari 23 citra sediaan darah tebal (thick blood film) sebanyak 91.5% (21 citra) tersegmentasi dengan baik. Dengan hasil tersebut dapat disimpulkan bahwa sistem segmentasi otomatis parasit malaria pada sedian darah tebal dengan menggunakan operasi morfologi dapat digunakan sebagai salah satu alternatif dalam proses segmentasi parasit malaria.  Kata Kunci: Thick Blood Film, Parasit Malaria, Operasi Morfologi. DOI : https://doi.org/10.33005/scan.v13i3.136

Performance Analysis of Color Cascading Framework on Two Different Classifiers in Malaria Detection

Malaria, as a dangerous disease globally, can be reduced its number of victims by finding a method of infection detection that is fast and reliable. Computer-based detection methods make it easier to identify the presence of plasmodium in blood smear images. This kind of methods is suitable for use in locations far from the availability of health experts. This study explores the use of two methods of machine learning on Cascading Color Framework, ie Backpropagation Neural Network and Support Vector Machine. Both methods were used as classifier in detecting malaria infection. From the experimental results it was found that Cascading Color Framework improved the classifier performance for both in Support Vector Machine and Backpropagation Neural Network

Erythrocyte Features for Malaria Parasite Detection in Microscopic Images of Thin Blood Smear: A Review

Microscopic image analysis of blood smear plays a very important role in characterization of erythrocytes in screening of malaria parasites. The characteristics feature of erythrocyte changes due to malaria parasite infection. The microscopic features of the erythrocyte include morphology, intensity and texture. In this paper, the different features used to differentiate the non- infected and malaria infected erythrocyte have been reviewed

SISTEM PENGONTROL KELEMBABAN TANAMAN ANGGREK MENGGUNAKAN TELEGRAM

Abstrak. Pengatur kelembaban otomatis sangat mendukung di dunia pertanian maupun tanaman hias yang sedang tren di kalangan perkotaan namun ada beberapa tantangan yang perlu dicarikan solusi terkait dengan perlakuan tanaman yang masing masing berbeda menurut tempat asalnya , mulai dari media tanam, intensitas cahaya yang di butuhkan kelembaban, suhu dan penyiramannya termasuk tanaman anggrek. Pada penelitian ini memanfaatkan mikrokontroller wemos D1 mini dengan modul sensor DHT11 sebagai pembaca nilai kelembaban dan relay sebagai pemutus aliran listrik pompa air yang terhubung dengan telegram secara online. Pengguna bisa mengontrol penyiraman tanaman anggrek dan membaca kelembaban melalui aplikasi telegram, dengan kesimpulan bahwa sistem ini mampu menambah kelembaban yang dibutuhkan oleh tanaman anggrek.  Kata Kunci: Kontrol kelembaban, wemos D1 mini, DHT11, Telegram DOI : https://doi.org/10.33005/scan.v13i3.145

Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation

In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations

PENERAPAN EKSTRAKSI CIRI ORDE SATU UNTUK KLASIFIKASI TEKSTUR MOTIF BATIK PESISIR DENGAN ALGORITMA BACKPROPAGASI

Wilayah pesisir pantai pulau Jawa yang meliputi kota Brebes, Cirebon, Pekalongan, Lasem dan Madura memiliki pola motif batik yang beragam. Berdasarkan ragamnya, motif batik pesisir dibedakan menjadi batik geometri dan nongeometri. Pada awal pengolahan citra batik ini   dilakukan cropping secara manual pada citra batik pesisir dengan merubah ukuran piksel menjadi 60 x 60 piksel. Dilanjutkan greyscaling pada citra cropping. Klasifikasi motif batik pesisir menggunakan algoritma bakpropagation dengan menentukan nilai  learning rate dan  momentum pada  saat  training data.  Data  inputan yang digunakan berupa  ciri  statistik  orde  satu.  Ciri  statistik  yang  digunakan antara  lain  mean,  kurtosis, skewness dan  enteropy. Hasil  uji  coba  menunjukkan learning rate  terbaik diperoleh pada  0,5  dan momentum 1,0 pada motif batik geometri. Sedangkan pada motif batik non geometri   learning rate terbaik diperoleh pada 0,5 dan momentum 1,0

Learning regions of interest from low level maps in virtual microscopy

Virtual microscopy can improve the workflow of modern pathology laboratories, a goal limited by the large size of the virtual slides (VS). Lately, determination of the Regions of Interest has shown to be useful in navigation and compression tasks. This work presents a novel method for establishing RoIs in VS, based on a relevance score calculated from example images selected by pathologist. The process starts by splitting the Virtual Slide (VS) into a grid of blocks, each represented by a set of low level features which aim to capture the very basic visual properties, namely, color, intensity, orientation and texture. The expert selects then two blocks i.e. A typical relevant (irrelevant) instance. Different similarity (disimilarity) maps are then constructed, using these positive (negative) examples. The obtained maps are then integrated by a normalization process that promotes maps with a similarity global maxima that largely exceeds the average local maxima. Each image region is thus entailed with an associated score, established by the number of closest positive (negative) blocks, whereby any block has also an associated score. Evaluation was carried out using 8 VS from different tissues, upon which a group of three pathologists had navigated. Precision-recall measurements were calculated at each step of any actual navigation, obtaining an average precision of 55% and a recall of about 38% when using the available set of navigations

Image processing and machine learning in the morphological analysis of blood cells

Introduction: This review focuses on how image processing and machine learning can be useful for the morphological characterization and automatic recognition of cell images captured from peripheral blood smears. Methods: The basics of the 3 core elements (segmentation, quantitative features, and classification) are outlined, and recent literature is discussed. Although red blood cells are a significant part of this context, this study focuses on malignant lymphoid cells and blast cells. Results: There is no doubt that these technologies may help the cytologist to perform efficient, objective, and fast morphological analysis of blood cells. They may also help in the interpretation of some morphological features and may serve as learning and survey tools. Conclusion: Although research is still needed, it is important to define screening strategies to exploit the potential of image-based automatic recognition systems integrated in the daily routine of laboratories along with other analysis methodologies.Peer ReviewedPostprint (published version