Segmentasi Citra Sel Darah Merah Berdasarkan Morfologi Sel Untuk Mendeteksi  Anemia Defisiensi Besi

Setiawan, Andika; Suryani, Esti; Wiharto, Wiharto

doi:10.20961/its.v3i1.638

Cited by 4 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Pengolahan citra digital merupakan pengolahan citra menggunakan perangkat komputer. Hal ini dilakukan agar mudah diinterpretasikan oleh manusia atau mesin (7). Self-organizing mapping (SOM) adalah salah satu pengolahan citra digital dengan aplikasi modern yang dapat digunakan dalam pengklasifikasian data (15).…”

Section: Bahasanunclassified

“…Simpulan: Nilai akurasi deteksi anemia menggunakan SOM dapat digunakan untuk mengidentifikasi tipe anemia berdasarkan ukuran sel darah merah. KATA KUNCI: anemia; jaringan saraf tiruan; deteksi citra darah; self-organizing mapping 85,71%; dan 86,58% (7). Selain itu, beberapa pengolahan digital dan jaringan saraf tiruan telah digunakan untuk menganalisis sel darah, baik untuk menghitung jumlah sel darah merah (8,9) maupun mendeteksi dan mengklasifikasikan berbagai tipe anemia berdasarkan bentuk sel darah merah (10).…”

unclassified

“…Analisis ini membutuhkan waktu lebih lama dengan konsentrasi yang tinggi. Sementara pemeriksaan laboratorium otomatis menggunakan alat tertentu tidak membutuhkan waktu lama, tetapi biaya yang diperlukan relatif mahal (5)(6)(7).…”

unclassified

See 2 more Smart Citations

Aplikasi self-organizing mapping sebagai alat deteksi anemia pada citra sel darah merah

Utari

Listyalina

Puspaningtyas

2019

Jurnal Gizi Klinik Indonesia

View full text Add to dashboard Cite

Application of self-organizing mapping as anemia detection using an image of red blood cellsBackground: Anemia is a nutritional problem characterized by changes in blood cell size, especially in microcytic or macrocytic anemia. Iron deficiency anemia is included in hypochromic microcytic anemia because it has a smaller than normal size red blood cell and has a lower than normal hemoglobin (Hb) arising from reduced supply of iron for erythropoiesis (cell maturation process red blood). Analysis based on red blood cell image is a tool to detect anemia using technology applications. Self-organizing mapping (SOM) is one of the artificial neural networks by dividing the input pattern into several groups, so the network output is in the form of groups that are most similar to the input.Objective: To measure the accuracy of SOM for detecting the size of red blood cells in anemia condition.Methods: The type of research was an observational laboratory. The study was conducted at the Electrobiomedical Laboratory of Universitas Respati Yogyakarta from January to August 2018. The sample consisted of anemia and non-anemia red blood cells which had been tested in a laboratory of 92 blood preparations. Stage of measuring red blood cells consisted of pre-processing (cropping, gray scaling, contrast enhancement, and screening), segmentation, feature extraction, and image identification with SOM. The image identification results were concluded by calculating the accuracy of the anemia detection system based on laboratory examination results.Results: The characteristic that distinguishes anemia and non-anemia was in the size of red blood cells. Anemic red blood cells had different pixel intensities than non-anemic red blood cells. The image of non-anemia red blood cells had a full round or oval image. From as many as 92 detections of blood images, five blood images were not by the target results of laboratory tests. The accuracy achieved by the system was 94.57%.Conclusions: The accuracy value of anemia detection using SOM can be used to identify the type of anemia based on red blood cell size.

show abstract

Section: Bahasanunclassified

unclassified

See 1 more Smart Citation

Aplikasi self-organizing mapping sebagai alat deteksi anemia pada citra sel darah merah

Utari

Listyalina

Puspaningtyas

2019

Jurnal Gizi Klinik Indonesia

View full text Add to dashboard Cite

show abstract

“…Penelitian terkait tentang pemanfaatan teknik pengolahan citra untuk mendeteksi adanya kelainan sel pada citra darah di antaranya identifikasi penyakit anemia defisiensi besi pada citra darah dengan menggunakan deteksi tepi Canny dan operasi morfologi [7]. Deteksi adanya kelainan sel darah salah satunya sel target dengan mengidentifikasi bentuk, kebundaran dan panjang sel [8].…”

Section: Pendahuluanunclassified

Identifikasi Anemia Thalasemia Betha (?) Mayor Berdasarkan Morfologi Sel Darah Merah

Suryani¹,

Wiharto²,

Wahyudiani³

2016

SJI

Self Cite

View full text Add to dashboard Cite

AbstrakThalasemia Betha (β) Mayor merupakan salah satu kelainan sel darah merah berdasarkan klasifikasi anemia. Thalasemia Betha (β) Mayor memiliki ciri sel meliputi mikrositik, eritrosit berinti (eritroblast), sel target, dan small fragment. Thalasemia Betha (β) Mayor dianalisa berdasarkan menghitung darah lengkap pada hapusan darah dengan pemeriksaan hematologi. Namun proses tersebut menimbulkan masalah, yaitu bahwa mempengaruhi keakuratan serta membutuhkan waktu. Untuk mengatasi hal tersebut, maka proses analisa dapat dilakukan dengan menggunakan teknik pengolahan citra yang didasarkan pada bentuk morfologi sel darah merah. Artikel ini bertujuan untuk mengidentifikasi anemia Thalasemia Betha (β) Mayor berdasarkan morfologi citra sel darah merah. Metode pengolahan citra yang digunakan meliputi proses segmentasi citra berupa normalisasi citra, threshold warna, threshold Otsu, operasi morfologi erosi dan bounding box. Normalisasi dilakukan untuk menyamakan persebaran warna pada masing-masing citra sel darah yang akan digunakan. Normalisasi dilakukan dengan mengubah citra RGB menjadi YCbCr. threshold warna, threshold Otsu, operasi morfologi erosi digunakan untuk memisahkan sel dan inti sel. Ekstrasi ciri menghasilkan luas area sel, luas area inti sel, diameter sel dan rasio sel. Proses identifikasi untuk menentukan jenis sel berdasarkan hasil ekstrasi ciri. Proses pengujian menggunakan 5 kelompok gambar Thalasemia Betha (β) Mayor yang masing-masing terdiri dari 6 gambar. Hasil pengujian menunjukkan bahwa morfologi citra mampu mengidentifikasi Anemia Thalasemia Betha (β) Mayor.Kata Kunci: Thalasemia Betha (β) Mayor, Identifikasi, Operasi morfologi PENDAHULUANSel darah merah (eritrosit) merupakan komponen darah yang jumlahnya paling banyak. Sel darah merah normal berbentuk cakram dengan kedua permukaannya cekung atau bikonkaf, tidak memiliki inti, dan mengandung hemoglobin. Kelainan pada sel darah merah terjadi karena sel darah merah dan/atau masa hemoglobin yang beredar tidak dapat memenuhi fungsinya untuk menyediakan oksigen bagi jaringan tubuh yang sering disebut dengan anemia [1]. Ada dua tipe anemia yaitu anemia gizi dan non-gizi. Anemia gizi terjadi akibat kekurangan gizi, sedangkan anemia non-gizi disebabkan oleh kelainan genetik [2]. Salah satu penyakit anemia non-gizi yang sering diderita adalah Thalasemia.

show abstract

“…Observation of white blood cells was carried out using a digital microscope by examining the smear stained with Giemsa, Wright, and May Grunwald. Identification of white blood cells is done manually, has shortcomings that are subjective depending on the workload and experience of staff, and requires a long time [3]. Automatic identification based on digital images [4] is expected to help identify cells quickly, precisely, and efficiently…”

Section: Introductionmentioning

confidence: 99%

Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction

Musliman

Fadlil

Yudhana

2021

join

View full text Add to dashboard Cite

In various disease diagnoses, one of the parameters is white blood cells, consisting of eosinophils, basophils, neutrophils, lymphocytes, and monocytes. Manual identification takes a long time and tends to be subjective depending on the staff's experience, so the automatic identification of white blood cells will be faster and more accurate. White blood cells are identified by examining a colored blood smear (SADT) and examined under a digital microscope to obtain a cell image. Image identification of white blood cells is determined through HSV color space segmentation (Hue, Saturation Value) and feature extraction of the Gray Level Cooccurrence Matrix (GLCM) method using the Angular Second Moment (ASM), Contrast, Entropy, and Inverse Different Moment (IDM) features. The purpose of this study was to identify white blood cells by comparing the classification accuracy of the K-nearest neighbor (KNN), Naïve Bayes Classification (NBC), and Multilayer Perceptron (MLP) methods. The classification results of 100 training data and 50 white blood cell image testing data. Tests on the KNN, NBC, and MLP methods yielded an accuracy of 82%, 80%, and 94%, respectively. Therefore, MLP was chosen as the best classification model in the identification of white blood cells.

show abstract

Segmentasi Citra Sel Darah Merah Berdasarkan Morfologi Sel Untuk Mendeteksi Anemia Defisiensi Besi

Cited by 4 publications

References 0 publications

Aplikasi self-organizing mapping sebagai alat deteksi anemia pada citra sel darah merah

Aplikasi self-organizing mapping sebagai alat deteksi anemia pada citra sel darah merah

Identifikasi Anemia Thalasemia Betha (?) Mayor Berdasarkan Morfologi Sel Darah Merah

Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction

Contact Info

Product

Resources

About